[ { "productId": "bugsnag", "storyId": "affected-users-quantified", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "BugSnag's stability metric explicitly measures 'the proportion of user application sessions that are error free,' and grouping is designed to show 'which issues are having the biggest impact on your users,' directly supporting blast-radius over raw counts; community corroboration confirms 'being able to track how many users are affected by a particular bug is amazing.' missing for 10: no explicit documentation of a per-issue 'affected users/sessions count' UI widget or independent hands-on verification of that exact metric display.", "evidenceIds": [ "bugsnag-docs-23", "bugsnag-docs-26", "bugsnag-docs-29", "bugsnag-comm-1" ] }, { "productId": "bugsnag", "storyId": "agent-triages-errors", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "BugSnag documents an official MCP server (SmartBear MCP) and OAuth support for it, plus stack-trace grouping and triage-like actions (fixed/snoozed/ignored, automatic assignment rules), which supports pulling and triaging top issues via MCP. However there is no evidence of a public REST/OpenAPI spec (probe returned 404s) and no documented integration that files bugs directly into an external tracker (e.g., Jira/GitHub Issues) — only Slack/PagerDuty/webhook integrations are evidenced. Missing for 10: documented API/OpenAPI spec for pulling issues, explicit issue-tracker (Jira/GitHub) filing integration, end-to-end example of an agent completing this workflow.", "evidenceIds": [ "bugsnag-docs-21", "bugsnag-probe-3", "bugsnag-docs-1", "bugsnag-docs-23", "bugsnag-docs-2", "bugsnag-docs-3", "bugsnag-docs-28", "bugsnag-probe-2" ] }, { "productId": "bugsnag", "storyId": "agentic-agent-docs", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "A direct probe found llms.txt returns 404, and there's no evidence of agent-oriented documentation elsewhere in the pack.", "evidenceIds": [ "bugsnag-probe-1" ] }, { "productId": "bugsnag", "storyId": "agentic-ai-insights", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence shows BugSnag has an MCP server for connecting external AI agents to its data (bugsnag-probe-3, bugsnag-docs-21), but nothing indicates BugSnag itself generates AI-driven insights, root-cause suggestions, or summaries inside its own dashboard. Features listed are manual grouping, stability metrics, alerting integrations, and release tracking — none framed as AI-generated. Missing for 10: any documented in-product AI insight/summary feature, AI-suggested fixes, or anomaly explanations generated by BugSnag itself.", "evidenceIds": [ "bugsnag-docs-21", "bugsnag-probe-3", "bugsnag-docs-23", "bugsnag-docs-5" ] }, { "productId": "bugsnag", "storyId": "agentic-autonomous-automation", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "BugSnag supports rule-based automations that run autonomously in the background, e.g. automatic error assignment rules, webhook data forwarding, and Slack/PagerDuty alerting triggered by error events, without requiring manual intervention. However these are traditional monitoring-integration automations, not AI-native/agentic workflows, and there is no evidence of scheduled or agent-driven autonomous tasks beyond reactive event rules. Missing for 10: evidence of AI-driven or scheduled autonomous workflows, agent-orchestrated automation, or user-configurable background jobs beyond simple assignment/webhook rules.", "evidenceIds": [ "bugsnag-docs-2", "bugsnag-docs-10", "bugsnag-docs-7", "bugsnag-docs-8", "bugsnag-docs-9" ] }, { "productId": "bugsnag", "storyId": "agentic-builtin-assistant", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is \"none\", never \"na\". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)", "evidenceIds": [] }, { "productId": "bugsnag", "storyId": "agentic-headless", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "BugSnag ships an official CLI (bugsnag-cli) explicitly meant for build integrations — e.g., creating builds and uploading source maps — which is designed to run in CI pipelines headlessly. However, this covers only release/source-map automation, not full headless operation of BugSnag's dashboard/alerting features, and there's no CI-specific tutorial (e.g., GitHub Actions example) or evidence of scripting the broader product beyond the CLI's build-upload function. Missing for 10: documented CI pipeline examples (GitHub Actions/Jenkins), evidence of headless automation beyond build/source-map upload, and API-driven automation docs for CI use cases.", "evidenceIds": [ "bugsnag-docs-12", "bugsnag-probe-4", "bugsnag-docs-11" ] }, { "productId": "bugsnag", "storyId": "agentic-mcp-client", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "BugSnag is an error-monitoring/observability SaaS product with no evidenced AI agent or assistant feature that would consume external tools; the only MCP-related evidence shows BugSnag exposing its own MCP server for others to query (bugsnag-docs-21, bugsnag-probe-3), which is the opposite direction of 'plugging MCP servers into this product.' Since BugSnag has no agentic/tool-consuming interface, this axis is a category error rather than an unmet capability.", "evidenceIds": [ "bugsnag-docs-21", "bugsnag-probe-3" ] }, { "productId": "bugsnag", "storyId": "agentic-mcp-server", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "BugSnag (via SmartBear) documents a self-hosted MCP server with OAuth 2.0 support and links to an official smartbear-mcp GitHub repo, confirming an official MCP server exists. missing for 10: first-party BugSnag docs detailing setup/usage of the MCP server itself, independent hands-on confirmation of connecting an agent successfully, and clarity on whether it's self-hosted only vs. also cloud-hosted.", "evidenceIds": [ "bugsnag-docs-21", "bugsnag-probe-3" ] }, { "productId": "bugsnag", "storyId": "agentic-nl-commands", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "BugSnag ships a self-hosted MCP server with OAuth support and SmartBear's official MCP server integration, which allows AI agents/assistants to interact with BugSnag data via natural-language-driven tool calls. However, there is no direct documentation describing a natural-language command interface, chat-based query capability, or examples of NL interactions with the product itself. missing for 10: explicit NL command examples/documentation, first-party description of conversational/agentic usage beyond the MCP server listing, independent corroboration of NL interaction working in practice.", "evidenceIds": [ "bugsnag-docs-21", "bugsnag-probe-3" ] }, { "productId": "bugsnag", "storyId": "agentic-official-cli", "verdict": "full", "quality": 6, "confidence": "medium", "rationale": "BugSnag ships an official CLI (bugsnag-cli) documented for build integrations, used to create builds and enrich releases in the dashboard. missing for 10: no evidence the CLI supports broader agentic/AI-native workflows beyond build/release tasks, and no independent hands-on corroboration of the CLI's usability.", "evidenceIds": [ "bugsnag-docs-12", "bugsnag-probe-4" ] }, { "productId": "bugsnag", "storyId": "agentic-public-api", "verdict": "partial", "quality": 4, "confidence": "medium", "rationale": "BugSnag exposes some programmatic surfaces — an official CLI for build/release automation (bugsnag-docs-12, bugsnag-probe-4), a webhook for data forwarding (bugsnag-docs-10), and a third-party-documented MCP server (bugsnag-probe-3) — suggesting some machine-drivable access. However, direct probes for a documented public REST/OpenAPI spec and an llms.txt both returned 404s (bugsnag-probe-1, bugsnag-probe-2), so there's no clear evidence of a comprehensive, documented public API. Missing for 10: an explicit REST/GraphQL API reference, official API docs page, and confirmation the MCP server is first-party rather than a separate SmartBear repo.", "evidenceIds": [ "bugsnag-docs-12", "bugsnag-probe-4", "bugsnag-docs-10", "bugsnag-probe-3", "bugsnag-probe-1", "bugsnag-probe-2" ] }, { "productId": "bugsnag", "storyId": "agentic-scoped-keys", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "There's mention of an MCP server with OAuth 2.0 support (bugsnag-docs-21, bugsnag-probe-3), but no evidence of scoped or least-privilege API credential issuance for agents specifically; no documentation of token scoping, permission granularity, or agent-specific credential management.", "evidenceIds": [] }, { "productId": "bugsnag", "storyId": "agentic-sdks", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "BugSnag ships official SDKs across 50+ platforms (JS, Node, React Native, iOS, Android, Flutter, etc.) with documented APIs like Bugsnag.notify(), source map support, and a CLI for build integration, giving AI-native developers robust, well-documented SDKs to build against. missing for 10: no OpenAPI/machine-readable spec (404s found), no llms.txt for AI-agent discoverability, and no independent hands-on developer review specifically validating SDK ergonomics for AI-native workflows.", "evidenceIds": [ "bugsnag-gh-1", "bugsnag-gh-2", "bugsnag-docs-16", "bugsnag-docs-31", "bugsnag-docs-11", "bugsnag-docs-12", "bugsnag-probe-4" ] }, { "productId": "bugsnag", "storyId": "agentic-webhooks", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "BugSnag's webhook/data-forwarding plugin gives access to error and project data as soon as events are received and processed, enabling event-driven subscription for automation/agentic workflows. missing for 10: independent hands-on confirmation of webhook payload structure/reliability, and no documented event-type filtering granularity beyond the basic description.", "evidenceIds": [ "bugsnag-docs-10" ] }, { "productId": "bugsnag", "storyId": "ai-fix-pull-request", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "BugSnag's evidence covers error monitoring, grouping, alerting, integrations, and an MCP server for querying data, but nothing indicates an AI capability that drafts code fixes or opens pull requests against a repo. Missing for 10: any AI code-fix generation feature, PR automation, or repo integration for fixes.", "evidenceIds": [ "bugsnag-docs-21", "bugsnag-probe-3", "bugsnag-docs-2" ] }, { "productId": "bugsnag", "storyId": "ai-root-cause", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows error grouping, stability tracking, and an MCP server for external AI integration, but nothing describes BugSnag's own AI analyzing stack traces/breadcrumbs/commits to propose a root cause. Missing for 10: any documented AI root-cause-analysis feature, first-party AI diagnosis output, or hands-on example of an AI-proposed fix.", "evidenceIds": [ "bugsnag-docs-21", "bugsnag-probe-3", "bugsnag-docs-23", "bugsnag-docs-20" ] }, { "productId": "bugsnag", "storyId": "alert-routing-channels", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "BugSnag documents Slack and PagerDuty integrations for error alerts and webhooks for custom routing, plus project/dashboard filtering and 'new issue' style status views. However, there is no evidence of configurable per-rule thresholds for frequency spikes or affected-user counts, nor explicit severity- or environment-based routing rules, or email alert configuration specifics. missing for 10: documented threshold-based alert rules (frequency spike, affected-user count), environment/severity-based routing configuration, email alerting evidence.", "evidenceIds": [ "bugsnag-docs-7", "bugsnag-docs-8", "bugsnag-docs-9", "bugsnag-docs-10", "bugsnag-docs-3", "bugsnag-docs-28" ] }, { "productId": "bugsnag", "storyId": "api-interactive-docs", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of an interactive API reference or runnable examples; probes explicitly show llms.txt and OpenAPI spec endpoints returning 404, and no docs mention a try-it-out console or code sandbox. missing for 10: interactive API reference UI, runnable/executable code examples, OpenAPI/Swagger spec availability.", "evidenceIds": [ "bugsnag-probe-1", "bugsnag-probe-2" ] }, { "productId": "bugsnag", "storyId": "api-machine-spec", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Direct probes for OpenAPI/swagger specs at expected paths all returned 404, and no documentation mentions a downloadable machine-readable API spec; missing for 10: any OpenAPI/Swagger file, spec download link, or documented API schema.", "evidenceIds": [ "bugsnag-probe-2" ] }, { "productId": "bugsnag", "storyId": "api-sandbox", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "BugSnag is an error-monitoring/observability tool, not a service with a sandboxed test API or environment concept; testing against sandbox vs production data is not an applicable axis for this kind of product.", "evidenceIds": [] }, { "productId": "bugsnag", "storyId": "api-versioning-policy", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence of a versioned API or documented deprecation policy; probes for OpenAPI spec and llms.txt both returned 404, and no docs reference API versioning or deprecation timelines.", "evidenceIds": [ "bugsnag-probe-2", "bugsnag-probe-1" ] }, { "productId": "bugsnag", "storyId": "automation-bulk-operations", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack mentions automation-adjacent features (assignment rules, event quota control, filtering by status) but nothing documents bulk actions on multiple errors/events at once (e.g., batch resolve, bulk delete, batch API endpoints). No CLI, API, or MCP documentation shows bulk operation support.", "evidenceIds": [ "bugsnag-docs-2", "bugsnag-docs-3", "bugsnag-docs-14", "bugsnag-docs-28" ] }, { "productId": "bugsnag", "storyId": "automation-rules-engine", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "BugSnag supports rule-based automation such as automatic error assignment rules, alerting integrations (Slack/PagerDuty/webhooks) that trigger on error events, and actioning errors from Slack, which together constitute event-triggered automated actions. However there's no evidence of a general-purpose, user-defined rules engine spanning arbitrary conditions/actions or AI-native/agentic triggering. missing for 10: a documented flexible rules/automation engine beyond assignment and alert integrations, evidence of custom multi-condition rule building, and independent confirmation of reliability.", "evidenceIds": [ "bugsnag-docs-2", "bugsnag-docs-7", "bugsnag-docs-8", "bugsnag-docs-9", "bugsnag-docs-10" ] }, { "productId": "bugsnag", "storyId": "automation-scheduled-jobs", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "BugSnag is an error monitoring/reporting tool, not a workflow/job scheduler; scheduling recurring jobs or workflows is outside its product category and no evidence suggests otherwise.", "evidenceIds": [] }, { "productId": "bugsnag", "storyId": "automation-versioned-workflows", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "BugSnag is an error monitoring/observability tool, not an automation-builder product; there are no 'automations' (workflows/rules) that a user would version, review, or roll back in the sense this story describes. The closest feature (error assignment rules) has no versioning/rollback capability documented, and this axis is a category mismatch for the product type.", "evidenceIds": [] }, { "productId": "bugsnag", "storyId": "breadcrumbs-custom-context", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Evidence shows BugSnag captures user/device/session context and correlates events (bugsnag-docs-27, bugsnag-docs-20) and supports reporting handled exceptions via Bugsnag.notify() (bugsnag-gh-2, bugsnag-gh-3), implying some contextual data is attached to events, but there is no explicit documentation of a breadcrumbs API or custom tags/metadata API in the pack. missing for 10: explicit breadcrumb trail API docs, custom tag/metadata attachment API docs, first-party example of setUser/addMetadata usage.", "evidenceIds": [ "bugsnag-docs-27", "bugsnag-docs-20", "bugsnag-gh-2", "bugsnag-gh-3" ] }, { "productId": "bugsnag", "storyId": "error-grouping-dedup", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Docs confirm automatic grouping via error class, file and line number of the top in-project stack frame (bugsnag-docs-1), which is the core fingerprinting story, and automatic-assignment rules exist (bugsnag-docs-2) but this addresses issue routing, not grouping override. No evidence is provided of a documented way to override/customize the grouping fingerprint itself (e.g. custom grouping hash) or of hands-on validation that grouping works well at scale. Missing for 10: explicit customization/override mechanism for grouping, independent/hands-on evidence of grouping accuracy at high volume.", "evidenceIds": [ "bugsnag-docs-1", "bugsnag-docs-2" ] }, { "productId": "bugsnag", "storyId": "error-trend-dashboards", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "BugSnag provides release-correlated error tracking, stability trend monitoring, and views of newly introduced errors and error grouping, which support elements of trend dashboards (bugsnag-docs-3, bugsnag-docs-4, bugsnag-docs-5, bugsnag-docs-25, bugsnag-docs-26, bugsnag-docs-29), and community feedback praises the dashboard experience (bugsnag-comm-6, bugsnag-comm-7). However, there is no explicit evidence of a cross-project/cross-team aggregated view or a dedicated 'top regressions' leaderboard spanning multiple teams — missing for 10: cross-project/team rollup dashboards, explicit 'top regressions' ranking feature, and independent validation of multi-team analytics at scale.", "evidenceIds": [ "bugsnag-docs-3", "bugsnag-docs-4", "bugsnag-docs-5", "bugsnag-docs-25", "bugsnag-docs-26", "bugsnag-docs-29", "bugsnag-comm-6", "bugsnag-comm-7" ] }, { "productId": "bugsnag", "storyId": "event-search-queries", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Docs confirm filtering by release (bugsnag-docs-4) and by error status (bugsnag-docs-3, bugsnag-docs-28), showing some filtering capability, but there is no evidence of filtering by tag, environment, or custom properties, nor any documented query syntax for cross-project search. missing for 10: evidence of tag/environment/custom-property filters, a documented search query language, and cross-project search capability.", "evidenceIds": [ "bugsnag-docs-4", "bugsnag-docs-3", "bugsnag-docs-28" ] }, { "productId": "bugsnag", "storyId": "issue-tracker-sync", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack lists integrations for Slack, PagerDuty, webhooks, and automatic error assignment, but no mention of Jira, GitHub Issues, or Linear ticket linking, nor any two-way status sync behavior (auto-resolve on ticket close, reopen on regression).", "evidenceIds": [ "bugsnag-docs-2", "bugsnag-docs-7", "bugsnag-docs-8", "bugsnag-docs-9", "bugsnag-docs-10" ] }, { "productId": "bugsnag", "storyId": "issue-triage-workflow", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Docs confirm issue statuses (fixed, snoozed, ignored) and automated assignment rules, implying core triage actions exist (bugsnag-docs-2, bugsnag-docs-3, bugsnag-docs-28), but there is no explicit documentation describing automatic reopening of a resolved issue upon regression. Missing for 10: explicit regression-reopen documentation, hands-on/independent confirmation of the reopen behavior, and clear UI walkthrough of resolve/ignore/snooze workflow.", "evidenceIds": [ "bugsnag-docs-2", "bugsnag-docs-3", "bugsnag-docs-28" ] }, { "productId": "bugsnag", "storyId": "issues-api-read", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence in the pack documents a REST API for listing, querying, or updating issues, or fetching raw events via scoped tokens; probes for openapi/swagger docs at the docs site returned 404s, and no data-access API reference is cited. Missing for 10: documented Data Access API reference, scoped API token/auth details, endpoints for issue CRUD and event retrieval.", "evidenceIds": [ "bugsnag-probe-1", "bugsnag-probe-2" ] }, { "productId": "bugsnag", "storyId": "js-sourcemap-unminify", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "BugSnag documents JavaScript source map support to reverse minified stack traces (bugsnag-docs-11) and a dedicated bugsnag-cli for build integrations that creates builds/releases in BugSnag (bugsnag-docs-12, bugsnag-probe-4), which aligns with CI-based source map upload workflows. Missing for 10: explicit bundler plugin (webpack/rollup) documentation for source map upload, and independent/hands-on confirmation that CI-uploaded source maps correctly de-minify production traces.", "evidenceIds": [ "bugsnag-docs-11", "bugsnag-docs-12", "bugsnag-probe-4" ] }, { "productId": "bugsnag", "storyId": "merge-split-issues", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence only describes BugSnag's automatic grouping algorithm (same error class/file/line) and rule-based auto-assignment, but nothing about manually merging separate issues or splitting an over-grouped fingerprint. Missing for 10: any documentation or mention of manual merge/split controls for grouped errors.", "evidenceIds": [ "bugsnag-docs-1", "bugsnag-docs-23", "bugsnag-docs-2" ] }, { "productId": "bugsnag", "storyId": "mobile-crash-reporting", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Evidence shows BugSnag supports '50+ platforms' generally, iOS-specific monitoring (pre-main app starts) and Flutter Android/iOS performance metrics, and a community report confirms hands-on iOS crash capture with automatic fatal-error reporting, but nothing in the pack explicitly documents Android or NDK crash capture, nor spells out the device/OS/app-version context fields captured for reproduction. Missing for 10: explicit Android/NDK crash reporting documentation, description of captured device/OS/app-version metadata, and independent corroboration beyond one old iOS anecdote.", "evidenceIds": [ "bugsnag-docs-16", "bugsnag-docs-31", "bugsnag-docs-22", "bugsnag-docs-19", "bugsnag-comm-2" ] }, { "productId": "bugsnag", "storyId": "native-symbolication", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Evidence confirms BugSnag has a build-integration CLI (bugsnag-cli) that creates builds and enriches releases, and that JavaScript source maps are explicitly supported for symbolication, implying support for platform-specific symbol upload. However, the evidence pack never explicitly mentions dSYM, ProGuard, or native debug symbol upload/processing. Missing for 10: explicit documentation of dSYM upload workflow, ProGuard/R8 mapping file upload, and confirmation that native (iOS/Android) crashes are symbolicated to real function names/lines.", "evidenceIds": [ "bugsnag-docs-11", "bugsnag-docs-12", "bugsnag-probe-4" ] }, { "productId": "bugsnag", "storyId": "noise-spike-controls", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "BugSnag documents error status actions with fixed/snoozed/ignored states (mute/ignore rules) and event-usage controls that let teams limit which events count toward quota, plus PagerDuty/Slack alert routing. However there is no evidence of true 'spike protection' (automatic surge suppression) or per-key/per-error rate limiting to prevent alert storms during a bad deploy. missing for 10: explicit spike protection feature, per-key rate limiting, deploy-aware alert throttling, independent confirmation these noise-control tools work as intended.", "evidenceIds": [ "bugsnag-docs-28", "bugsnag-docs-3", "bugsnag-docs-14", "bugsnag-docs-24", "bugsnag-docs-9", "bugsnag-docs-7" ] }, { "productId": "bugsnag", "storyId": "open-ingest-protocol", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "There is no evidence of a documented open/public event-ingestion API/protocol spec that would let a custom client send events without using an official SDK; probes for llms.txt and OpenAPI spec both returned 404, and all evidence points to official SDKs (bugsnag-js, 50+ platform libraries) rather than a documented wire protocol.", "evidenceIds": [ "bugsnag-probe-1", "bugsnag-probe-2", "bugsnag-docs-31" ] }, { "productId": "bugsnag", "storyId": "openness-api-parity", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows only an SDK for reporting errors (bugsnag-gh-2, docs-11), a CLI for build/release metadata (bugsnag-docs-12), and a third-party MCP server (bugsnag-probe-3), but no documented Data/Management API letting users perform dashboard actions like assigning, snoozing, configuring stability targets, or managing integrations programmatically. Probes for an OpenAPI spec and llms.txt both returned 404 (bugsnag-probe-1, bugsnag-probe-2), indicating no public comprehensive API is discoverable.", "evidenceIds": [ "bugsnag-probe-1", "bugsnag-probe-2", "bugsnag-docs-12", "bugsnag-probe-3", "bugsnag-gh-2" ] }, { "productId": "bugsnag", "storyId": "openness-full-export", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence of a data export feature or open-format data portability for BugSnag; the closest is webhooks/data-forwarding for real-time integration, not bulk export/leave capability. Probes for llms.txt and OpenAPI both return 404, and no docs mention exporting historical error data in open formats.", "evidenceIds": [ "bugsnag-docs-10", "bugsnag-probe-1", "bugsnag-probe-2" ] }, { "productId": "bugsnag", "storyId": "openness-open-license", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Only client-side SDK/integration code (e.g., bugsnag-js) is published on GitHub under an open license; the core BugSnag error-monitoring platform, dashboard, and backend remain closed-source proprietary SaaS. Missing for 10: open licensing/source access to the core product itself, not just client libraries; no license file evidence cited; no independent confirmation of full source availability.", "evidenceIds": [ "bugsnag-gh-1", "bugsnag-gh-2" ] }, { "productId": "bugsnag", "storyId": "openness-self-host", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "BugSnag On-Premise offers a documented self-hosted version that can run in existing PCI/HIPAA compliant environments, directly addressing self-hosting the core product. Missing for 10: independent hands-on verification of the on-prem deployment, detailed setup/architecture docs, and confirmation of feature parity with the SaaS offering.", "evidenceIds": [ "bugsnag-docs-13" ] }, { "productId": "bugsnag", "storyId": "privacy-data-residency", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "BugSnag's On-Premise offering lets organizations self-host and 'manage their own data' in PCI/HIPAA compliant environments, which indirectly satisfies data residency needs, but there is no explicit evidence of a cloud-based region selection feature (e.g., EU vs US data storage) for standard plans. Missing for 10: explicit multi-region hosting options, documented data residency controls for the standard SaaS offering, and any first-party statement about region choice at signup.", "evidenceIds": [ "bugsnag-docs-13", "bugsnag-docs-6" ] }, { "productId": "bugsnag", "storyId": "privacy-no-training", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "BugSnag is an error-monitoring product with data redaction/compliance tools and on-premise hosting, but no evidence addresses AI model training data usage policies or opt-outs. This is an applicable privacy-posture question for any SaaS handling customer data, so absence of evidence yields 'none'.", "evidenceIds": [] }, { "productId": "bugsnag", "storyId": "privacy-retention-controls", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "BugSnag documents retroactive redaction/removal of sensitive data for GDPR/CCPA/HIPAA compliance, event usage controls to limit which events are saved/counted, and self-hosted/on-premise deployment for organizations wanting full control over their data—together covering meaningful retention/deletion controls. However, there is no explicit documentation of configurable data retention periods, automated deletion schedules, or a dedicated data-deletion API/workflow. Missing for 10: explicit retention period settings, automated/scheduled deletion policies, and independent verification of these controls in practice.", "evidenceIds": [ "bugsnag-docs-6", "bugsnag-docs-30", "bugsnag-docs-14", "bugsnag-docs-13" ] }, { "productId": "bugsnag", "storyId": "privacy-telemetry-optout", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack addresses telemetry/usage-tracking opt-out settings for BugSnag itself; the closest items concern event quota controls and sensitive data redaction within monitored data, not opting out of BugSnag's own product telemetry.", "evidenceIds": [] }, { "productId": "bugsnag", "storyId": "quota-spend-controls", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "BugSnag docs mention giving customers control over which events count toward their event quota (bugsnag-docs-14), and community pricing discussion confirms plan-based event caps exist (bugsnag-comm-3, bugsnag-comm-4), but there is no explicit documentation of setting a spend cap or hard budget limit to prevent overage billing. missing for 10: explicit spend-limit/budget-cap feature documentation, engineering-leader-facing quota configuration UI, confirmation that exceeding quota does not trigger surprise charges.", "evidenceIds": [ "bugsnag-docs-14", "bugsnag-comm-3", "bugsnag-comm-4" ] }, { "productId": "bugsnag", "storyId": "regression-alerts", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence covers error status states (fixed/snoozed/ignored), release annotations, and stability tracking, but nowhere describes a distinct 'regression' alert that fires specifically when a previously fixed error reappears in a new release, separate from ordinary new-issue notifications. Without that specific capability documented, this applicable SRE-alerting axis is unsupported by the evidence pack.", "evidenceIds": [ "bugsnag-docs-3", "bugsnag-docs-28", "bugsnag-docs-4", "bugsnag-docs-25" ] }, { "productId": "bugsnag", "storyId": "release-health-crash-free", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "BugSnag documents stability monitoring (proportion of error-free sessions, i.e. crash-free sessions/users) with configurable stability targets, plus release filtering and release annotations on the timeline to correlate new versions with error-rate changes. However, there is no explicit evidence of an 'adoption per release' metric or a dedicated canary-vs-stable release comparison view/report. Missing for 10: adoption-per-release tracking, explicit canary vs stable release comparison feature, independent corroboration of these release-health workflows.", "evidenceIds": [ "bugsnag-docs-5", "bugsnag-docs-26", "bugsnag-docs-29", "bugsnag-docs-4", "bugsnag-docs-25" ] }, { "productId": "bugsnag", "storyId": "release-tracking-commits", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Docs show BugSnag tracks releases/builds and lets you filter by release to see which release an error was introduced in (docs-4, docs-12, docs-3), supporting the 'first appeared in release' half of the story. There is no evidence of a 'suspect commit' feature that pinpoints the likely causing commit for an issue. Missing for 10: suspect-commit identification, commit-level blame linking, independent corroboration of release-regression accuracy.", "evidenceIds": [ "bugsnag-docs-4", "bugsnag-docs-12", "bugsnag-docs-3" ] }, { "productId": "bugsnag", "storyId": "scm-blame-context", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack shows error grouping, stability tracking, source maps for JS deobfuscation, and various notification integrations, but nothing about connecting to source repos (GitHub/GitLab/Bitbucket) to surface suspect commits, code owners, or linking stack frames to exact lines on a default branch. missing for 10: repo/VCS integration setup, suspect commit detection, code owner assignment from repo data, stack-frame-to-source-line deep links.", "evidenceIds": [ "bugsnag-docs-11", "bugsnag-docs-23", "bugsnag-docs-2" ] }, { "productId": "bugsnag", "storyId": "sdk-broad-coverage", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "BugSnag provides official SDKs for web/JS frameworks, Node.js/backend, and mobile (React Native, iOS, Flutter) that automatically detect uncaught errors, plus notify() for handled exceptions, and docs confirm 50+ platform libraries with independent community corroboration of ease of automatic crash capture. Missing for 10: no independent hands-on verification specifically for backend/mobile automatic capture beyond docs, only one community quote about iOS auto-reporting.", "evidenceIds": [ "bugsnag-gh-1", "bugsnag-gh-2", "bugsnag-gh-3", "bugsnag-docs-16", "bugsnag-docs-31", "bugsnag-comm-2" ] }, { "productId": "bugsnag", "storyId": "server-side-data-scrubbing", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "BugSnag documents server-side redaction/removal tools for sensitive data retroactively (sensitive-data-management docs) which addresses server-side scrubbing, but the evidence pack lacks explicit documentation of SDK-level filtering/redaction rules applied before payload send (e.g., filters config) and no independent/hands-on corroboration of scrubbing effectiveness is present. missing for 10: SDK-level pre-send filtering documentation, independent verification that PII never persists, and confirmation rules apply before data storage rather than only retroactively.", "evidenceIds": [ "bugsnag-docs-6", "bugsnag-docs-30" ] }, { "productId": "bugsnag", "storyId": "user-feedback-on-errors", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "BugSnag's evidence covers error detection, grouping, alerting, and stability metrics, but there is no mention of a user-facing feedback dialog/widget for capturing end-user comments tied to a crash event — a feature other crash reporters (e.g., Sentry's User Feedback) explicitly ship. No evidence of any feedback-collection UI or API attaching user input to error events.", "evidenceIds": [] }, { "productId": "glitchtip", "storyId": "affected-users-quantified", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence GlitchTip surfaces user/session impact metrics (affected users, session counts, or blast-radius prioritization) on issues; docs cover error tracking, performance transactions, alerts, and MCP/CLI tooling but nothing about quantifying user/session impact per issue. Missing for 10: any mention of affected-user counts, session tracking, or blast-radius based prioritization/sorting.", "evidenceIds": [ "glitchtip-docs-1", "glitchtip-docs-6", "glitchtip-docs-15" ] }, { "productId": "glitchtip", "storyId": "agent-triages-errors", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "GlitchTip ships a built-in MCP server that lets AI assistants browse issues, inspect stack traces/event data, and resolve issues, and is documented as Sentry-API-compatible for programmatic access, covering the core 'pull issues with stack traces and triage' portion of the story. However, there's no evidence of a filed-bug/tracker-integration capability (e.g., auto-filing into Jira/GitHub) or of a prioritized 'top production issues' query, and probes show no public OpenAPI spec, weakening independent verification of the API path. Missing for 10: documented ticket-filing/tracker integration from the agent, evidence of 'top issues' ranking via API/MCP, and a public/verifiable API spec.", "evidenceIds": [ "glitchtip-docs-9", "glitchtip-docs-10", "glitchtip-docs-19", "glitchtip-docs-11", "glitchtip-probe-4", "glitchtip-probe-3" ] }, { "productId": "glitchtip", "storyId": "agentic-agent-docs", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Direct probes confirm llms.txt (404) and machine-readable docs/openapi endpoints (404) are absent; while GlitchTip has an MCP server, there is no evidence of an llms.txt file or agent-oriented documentation format that an agent could be pointed at.", "evidenceIds": [ "glitchtip-probe-1", "glitchtip-probe-2", "glitchtip-probe-3" ] }, { "productId": "glitchtip", "storyId": "agentic-ai-insights", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "GlitchTip's built-in MCP server lets external AI assistants query error, performance, and log data and includes analysis tools like detect_n_plus_one for pattern detection, which can surface AI-generated insights from the data. However, this is enabling external AI tools to analyze data rather than the product itself generating insights or suggestions natively inside its own UI. Missing for 10: in-product AI insight/summary generation, evidence of natively surfaced suggestions (e.g., root-cause hints) within the GlitchTip dashboard itself, and independent confirmation of MCP-driven insight quality.", "evidenceIds": [ "glitchtip-docs-9", "glitchtip-docs-19", "glitchtip-docs-20", "glitchtip-docs-10" ] }, { "productId": "glitchtip", "storyId": "agentic-autonomous-automation", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "GlitchTip's automation is limited to built-in alerting (email/webhook on errors, uptime pings) and its MCP server is described only as a way to 'investigate' and query error data, not to configure or run autonomous background workflows on behalf of an AI agent. There is no evidence of an AI-driven automation/workflow builder or scheduled agentic tasks that a user could set up.", "evidenceIds": [ "glitchtip-docs-7", "glitchtip-docs-16", "glitchtip-docs-19", "glitchtip-docs-10" ] }, { "productId": "glitchtip", "storyId": "agentic-builtin-assistant", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "GlitchTip ships an MCP server so external AI tools/assistants can query its error data, but there is no evidence of a built-in AI assistant inside the product itself that a user could delegate tasks to — the direction is reversed (external AI pulls from GlitchTip, not GlitchTip offering its own assistant).", "evidenceIds": [ "glitchtip-docs-9", "glitchtip-docs-19", "glitchtip-docs-10" ] }, { "productId": "glitchtip", "storyId": "agentic-headless", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "GlitchTip ships an official CLI (glitchtip-cli) with commands for login, creating releases, recording deployments, and uploading debug files/source maps — all standard headless CI automation tasks (e.g. glitchtip-cli releases new 1.0.0 --org --project, glitchtip-cli deploys new). This directly supports scripted/CI use even though it's framed as error-tracking tooling rather than an agent runtime. Missing for 10: explicit CI/CD pipeline examples (e.g. GitHub Actions/GitLab CI snippets), independent community confirmation of CI usage, and no evidence of a broader headless server/automation mode beyond the CLI.", "evidenceIds": [ "glitchtip-docs-5", "glitchtip-docs-21", "glitchtip-docs-22", "glitchtip-docs-27", "glitchtip-probe-5" ] }, { "productId": "glitchtip", "storyId": "agentic-mcp-client", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Evidence shows GlitchTip exposes its own built-in MCP server so external AI assistants can pull GlitchTip data (server role), but there is no evidence GlitchTip itself can consume/plug in external MCP servers to use their tools (client role), which is what this story requires.", "evidenceIds": [ "glitchtip-docs-9", "glitchtip-docs-19", "glitchtip-probe-4" ] }, { "productId": "glitchtip", "storyId": "agentic-mcp-server", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "GlitchTip documents a built-in, official MCP server enabling AI assistants to interact with error tracking, performance, and log data, with specific tools like investigating errors and detecting N+1 queries. This is first-party documented and confirmed by a dedicated docs page and probe. Missing for 10: independent/hands-on community corroboration of the MCP server working in practice, and more detail on setup/authentication.", "evidenceIds": [ "glitchtip-docs-9", "glitchtip-docs-10", "glitchtip-docs-19", "glitchtip-docs-20", "glitchtip-probe-4" ] }, { "productId": "glitchtip", "storyId": "agentic-nl-commands", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "GlitchTip ships a built-in MCP server that lets AI assistants browse issues, inspect stack traces/event data, resolve issues, and even detect N+1 query patterns, enabling natural-language interaction via connected AI tools rather than the raw UI. However, this is entirely first-party documentation with no independent/hands-on confirmation, and the documented scope is limited to investigation/read-type operations rather than full product control via NL. Missing for 10: independent verification of the MCP server working in practice, evidence of broader write/config operations (alerts, settings) via natural language, and any non-vendor usage reports.", "evidenceIds": [ "glitchtip-docs-9", "glitchtip-docs-10", "glitchtip-docs-19", "glitchtip-docs-20", "glitchtip-probe-4" ] }, { "productId": "glitchtip", "storyId": "agentic-official-cli", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "GlitchTip ships an official glitchtip-cli with documented commands for login, releases, deployments, and debug-file uploads, confirmed by dedicated CLI docs and a probe. However, it's explicitly framed around release/source-map management rather than general agentic/AI-native workflows, and the CLI itself is separate from the MCP server (the AI-facing feature); no independent hands-on reviews of the CLI were found. Missing for 10: independent/community verification of the CLI's reliability, evidence of AI-native or agentic use cases for the CLI specifically, and confirmation it's out of beta.", "evidenceIds": [ "glitchtip-docs-5", "glitchtip-docs-21", "glitchtip-docs-22", "glitchtip-docs-27", "glitchtip-probe-5", "glitchtip-docs-2" ] }, { "productId": "glitchtip", "storyId": "agentic-public-api", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "GlitchTip claims Sentry API compatibility (docs-11) and ships a documented CLI (docs-5,21,22,27) and an MCP server explicitly built for AI assistants to query error/performance/log data (docs-9,10,19,20), all supporting programmatic/AI-driven access. However, probes found no discoverable OpenAPI/Swagger spec or public API reference (probe-3), and no llms.txt (probe-1), so the actual 'documented public API' surface is thin and inferred mainly from SDK/CLI/MCP docs rather than a first-class API reference. Missing for 10: an explicit REST/GraphQL API reference doc, independent/community confirmation of API-driven automation, and resolution of the 404s on OpenAPI/swagger endpoints.", "evidenceIds": [ "glitchtip-docs-11", "glitchtip-docs-5", "glitchtip-docs-9", "glitchtip-docs-19", "glitchtip-docs-20", "glitchtip-probe-3", "glitchtip-probe-4", "glitchtip-probe-5" ] }, { "productId": "glitchtip", "storyId": "agentic-scoped-keys", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "GlitchTip documents an MCP server and CLI with login/org/project scoping, but there is no evidence of scoped or least-privilege API credential/token issuance specifically for agent use (e.g., read-only or capability-limited tokens for MCP/CLI); docs only describe org/project selection and general auth, not credential scoping. Missing for 10: documentation of granular/least-privilege API key or token scopes, any agent-specific credential model, or admin controls to restrict an agent's access.", "evidenceIds": [ "glitchtip-docs-19", "glitchtip-docs-27", "glitchtip-probe-4", "glitchtip-probe-5" ] }, { "productId": "glitchtip", "storyId": "agentic-sdks", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "GlitchTip officially documents that it works with Sentry's open-source SDKs and aims for full Sentry API compatibility, meaning developers build against the same official, widely-used SDK ecosystem; this is corroborated by community users who confirm reliable use of Sentry client libraries with GlitchTip in production. Missing for 10: GlitchTip-specific SDK documentation/language coverage list, and no independent verification of parity across all Sentry SDK versions/features.", "evidenceIds": [ "glitchtip-docs-1", "glitchtip-docs-11", "glitchtip-comm-1", "glitchtip-comm-2" ] }, { "productId": "glitchtip", "storyId": "agentic-webhooks", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "GlitchTip lets users add a webhook URL as an alert recipient so external systems can be notified of new errors/downtime pings, which satisfies the basic 'subscribe via webhook' capability. However, documentation is thin—no payload schema, event-type filtering, retry/delivery guarantees, or independent/hands-on confirmation of webhook use are provided. Missing for 10: webhook payload format documentation, granular event-type subscription options, delivery reliability details, and community corroboration of webhook usage.", "evidenceIds": [ "glitchtip-docs-4", "glitchtip-docs-7", "glitchtip-docs-16" ] }, { "productId": "glitchtip", "storyId": "ai-fix-pull-request", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "GlitchTip's MCP server lets AI tools read error data (investigate issues, detect N+1 queries) but there is no evidence of AI drafting code fixes or opening pull requests against a repo; this is an error-tracking platform, not a code-fix automation tool.", "evidenceIds": [ "glitchtip-docs-9", "glitchtip-docs-10", "glitchtip-docs-19", "glitchtip-docs-20" ] }, { "productId": "glitchtip", "storyId": "ai-root-cause", "verdict": "partial", "quality": 3, "confidence": "medium", "rationale": "GlitchTip ships a built-in MCP server that exposes stack traces, issues, and event data (including an N+1 query detector) to external AI tools, enabling an AI assistant to investigate an issue — but there is no evidence of GlitchTip itself analyzing breadcrumbs plus related commits to propose an actionable root cause; that analysis would rely entirely on whatever third-party AI client is plugged in via MCP. Missing for 10: native root-cause synthesis combining commits/breadcrumbs/stack trace, any documented example of an AI-generated root-cause suggestion, and evidence of commit/VCS integration feeding the MCP tools.", "evidenceIds": [ "glitchtip-docs-9", "glitchtip-docs-10", "glitchtip-docs-19", "glitchtip-docs-20", "glitchtip-probe-4" ] }, { "productId": "glitchtip", "storyId": "alert-routing-channels", "verdict": "partial", "quality": 3, "confidence": "medium", "rationale": "GlitchTip supports project-level alert routing (via teams) and basic recipients (email by default, plus adding a webhook URL), which could technically forward to Slack/PagerDuty, but there is no documented support for environment- or severity-based routing, nor per-rule thresholds like frequency spikes or affected-user counts. Missing for 10: environment/severity-based routing rules, native Slack/PagerDuty integrations, configurable thresholds for new-issue/frequency-spike/affected-user-count alerts.", "evidenceIds": [ "glitchtip-docs-3", "glitchtip-docs-4", "glitchtip-docs-18" ] }, { "productId": "glitchtip", "storyId": "api-interactive-docs", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "There is no evidence of an interactive API reference or runnable examples; probes explicitly show 404s for OpenAPI/swagger specs and no docs-as-markdown or llms.txt, and no mention of a sandboxed/try-it-out API explorer.", "evidenceIds": [ "glitchtip-probe-3", "glitchtip-probe-1", "glitchtip-probe-2" ] }, { "productId": "glitchtip", "storyId": "api-machine-spec", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence GlitchTip publishes an OpenAPI/Swagger spec; direct probes for openapi.json, swagger.json, and related paths all returned 404. Claims of 'Sentry API compatibility' do not constitute a machine-readable spec for GlitchTip itself.", "evidenceIds": [ "glitchtip-probe-3", "glitchtip-docs-11" ] }, { "productId": "glitchtip", "storyId": "api-sandbox", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "GlitchTip is an error-tracking/monitoring platform (self-hosted Sentry-compatible); there is no evidence of a dedicated sandbox/test environment feature for isolating test traffic from production data. Self-hosting could theoretically create a separate instance, but no documentation describes a sandbox mode or test-data isolation capability.", "evidenceIds": [] }, { "productId": "glitchtip", "storyId": "api-versioning-policy", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of API versioning scheme or a documented deprecation policy; probes for OpenAPI spec and docs manifest all 404'd, and no changelog/policy text addresses API stability or deprecation windows.", "evidenceIds": [ "glitchtip-probe-3", "glitchtip-probe-1", "glitchtip-probe-2" ] }, { "productId": "glitchtip", "storyId": "automation-bulk-operations", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence of bulk operations (e.g., bulk resolve/delete/tag issues across many items) via UI, CLI, API, or MCP server; the CLI and MCP tools described operate on single releases/projects/issues, not bulk batches.", "evidenceIds": [] }, { "productId": "glitchtip", "storyId": "automation-rules-engine", "verdict": "partial", "quality": 4, "confidence": "medium", "rationale": "GlitchTip supports basic event-triggered alerting: new-issue alerts default to emailing team members, but you can add webhook URLs as alert recipients, and uptime monitoring can also fire alerts via email/webhook when a ping fails. This is a simple automatic action-on-event mechanism, but there's no evidence of a broader rule engine (conditions, multi-step workflows, non-alert actions) beyond notify-via-email/webhook. Missing for 10: conditional rule builder, support for triggering arbitrary actions (not just notifications), evidence of chained/automated workflows, and independent confirmation of rule reliability.", "evidenceIds": [ "glitchtip-docs-4", "glitchtip-docs-7", "glitchtip-docs-16" ] }, { "productId": "glitchtip", "storyId": "automation-scheduled-jobs", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "GlitchTip is an error-tracking/monitoring tool; its uptime ping feature is a monitoring check, not a mechanism for scheduling arbitrary recurring jobs or workflows. No evidence shows any job scheduler, cron-like workflow automation, or recurring task orchestration for AI-native use cases.", "evidenceIds": [ "glitchtip-docs-7", "glitchtip-docs-16" ] }, { "productId": "glitchtip", "storyId": "automation-versioned-workflows", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "GlitchTip is an error-tracking/monitoring platform, not an automation/workflow builder; there is no concept of 'automations' to version, review, or roll back. This story targets a category (automation platforms) that GlitchTip does not belong to.", "evidenceIds": [] }, { "productId": "glitchtip", "storyId": "breadcrumbs-custom-context", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "GlitchTip relies on Sentry's official SDKs for event capture and explicitly claims Sentry API compatibility ('Anything that works with Sentry should also work with GlitchTip'), which implies inherited support for breadcrumbs, tags, and user context since these are core Sentry SDK features. However, the evidence pack never explicitly documents these specific instrumentation primitives (breadcrumbs, tag API, user context) on GlitchTip's own docs. Missing for 10: explicit GlitchTip documentation demonstrating breadcrumb capture, custom tag attachment, and user context APIs, plus hands-on confirmation these render correctly in the GlitchTip UI.", "evidenceIds": [ "glitchtip-docs-1", "glitchtip-docs-11", "glitchtip-comm-1", "glitchtip-comm-2" ] }, { "productId": "glitchtip", "storyId": "error-grouping-dedup", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack covers SDK ingestion, source maps, alerting, CLI, and MCP server features, but never mentions stack-trace fingerprinting, issue grouping of duplicate events, or any custom fingerrule/grouping override capability. The general 'Sentry API compatible' claim (glitchtip-docs-11) does not concretely document grouping behavior or customization tools.", "evidenceIds": [] }, { "productId": "glitchtip", "storyId": "error-trend-dashboards", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence covers issue creation, teams/projects, CLI-based release tracking, and an MCP server for browsing individual errors, but there is no mention of cross-project/team dashboards, top-regression views, new-issues-per-release rollups, or volume-over-time charts. In fact one doc explicitly states GlitchTip avoids 'dashboard building and metrics hunting,' suggesting this kind of leadership-level trend dashboard is not a focus. missing for 10: cross-project/team aggregate dashboards, top-regression identification, new-issues-per-release reporting, volume-over-time visualizations.", "evidenceIds": [ "glitchtip-docs-6", "glitchtip-docs-18", "glitchtip-docs-10", "glitchtip-docs-21", "glitchtip-comm-5" ] }, { "productId": "glitchtip", "storyId": "event-search-queries", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence describes a search/query syntax for filtering events by tag, release, environment, or custom properties; documentation covers SDK integration, alerts, CLI releases, MCP server, and performance monitoring but not a query/search feature across projects.", "evidenceIds": [] }, { "productId": "glitchtip", "storyId": "issue-tracker-sync", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of Jira/GitHub Issues/Linear integrations with two-way status sync; GlitchTip's documented integrations cover GitLab error tracking, Grafana data source, and an MCP server, none of which mention issue tracker linking or bidirectional resolve/reopen sync.", "evidenceIds": [ "glitchtip-docs-12", "glitchtip-docs-13", "glitchtip-docs-11" ] }, { "productId": "glitchtip", "storyId": "issue-triage-workflow", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Docs confirm issues can be 'resolved' via the MCP interface, and teams route notifications to owners, but there is no evidence of ignore/mute, snooze, explicit assignment workflow, or automatic reopening of a resolved issue upon regression. missing for 10: assign-to-user workflow, ignore/mute action, snooze/time-based silencing, automatic reopen-on-regression behavior.", "evidenceIds": [ "glitchtip-docs-10", "glitchtip-docs-18", "glitchtip-docs-19" ] }, { "productId": "glitchtip", "storyId": "issues-api-read", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "GlitchTip claims Sentry API compatibility, implying issue listing/query/update and raw event fetch via REST API with tokens, but there is no direct documentation of endpoints, scoping, or token permissions in the evidence, and probes for OpenAPI/API docs all returned 404s. missing for 10: dedicated REST API reference docs, explicit scoped-token/auth documentation, and confirmation of update/query endpoints beyond inferred Sentry compatibility.", "evidenceIds": [ "glitchtip-docs-11", "glitchtip-probe-3" ] }, { "productId": "glitchtip", "storyId": "js-sourcemap-unminify", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "GlitchTip explicitly documents uploading source maps via its CLI (Beta) with debug ID injection for readable JS stack traces, and the CLI supports release/deploy tracking consistent with CI workflows. However, evidence for a dedicated bundler plugin (webpack/vite/rollup) is absent, the CLI feature is marked Beta, and there's no independent/community confirmation of source map upload working in practice. Missing for 10: bundler plugin support, CI pipeline examples, hands-on/community verification of accurate deminification.", "evidenceIds": [ "glitchtip-docs-2", "glitchtip-docs-5", "glitchtip-docs-21", "glitchtip-probe-5" ] }, { "productId": "glitchtip", "storyId": "merge-split-issues", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence describes issue merge/unmerge or split functionality for fingerprinting-collapsed groups; the docs mention resolving issues, alerts, and MCP tooling for investigating errors, but nothing about merging or splitting grouped issues.", "evidenceIds": [] }, { "productId": "glitchtip", "storyId": "mobile-crash-reporting", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "GlitchTip relies on Sentry's open-source SDKs (which include official iOS/Android/NDK SDKs) and its CLI explicitly supports uploading debug symbols for native apps (dSYM, PDB, ELF) for crash symbolication, implying native mobile crash capture is possible. However, there is no explicit documentation of the mobile SDK setup, device/OS/app-version context capture, or any hands-on confirmation of iOS/Android/NDK crash reporting working end-to-end. Missing for 10: explicit iOS/Android SDK integration docs, device/OS/app-version context field documentation, and independent verification of native crash reproduction.", "evidenceIds": [ "glitchtip-docs-1", "glitchtip-docs-22", "glitchtip-docs-11" ] }, { "productId": "glitchtip", "storyId": "native-symbolication", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "GlitchTip's CLI explicitly supports uploading native debug symbols (dSYM, PDB, ELF) via `glitchtip-cli debug-files upload`, directly addressing native/mobile symbolication, and its Sentry-SDK compatibility implies ProGuard mapping support could work similarly to Sentry. However, there is no explicit documentation or community confirmation of ProGuard mapping upload or Android/Java symbolication specifically, and no hands-on evidence of successful symbolication for mobile crashes. Missing for 10: explicit ProGuard/Android mapping upload docs, independent confirmation of successful mobile crash symbolication.", "evidenceIds": [ "glitchtip-docs-22", "glitchtip-docs-11", "glitchtip-docs-5" ] }, { "productId": "glitchtip", "storyId": "noise-spike-controls", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence only shows basic alert routing (team notification emails/webhooks per project) and performance sampling via tracesSampleRate, but no mention of spike protection, per-key rate limiting, or mute/ignore/snooze rules for issues to combat alert fatigue.", "evidenceIds": [ "glitchtip-docs-4", "glitchtip-docs-18", "glitchtip-docs-23" ] }, { "productId": "glitchtip", "storyId": "open-ingest-protocol", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "GlitchTip explicitly claims Sentry API compatibility, meaning it reuses Sentry's open-source ingestion protocol/SDK ecosystem rather than a proprietary one, and community reports confirm it works reliably with standard Sentry SDKs including newer ones like Svelte. However, there is no first-party protocol specification, OpenAPI/Swagger schema, or raw DSN/envelope format documentation (probes for openapi.json and docs.md return 404), so a developer building a fully custom client without a Sentry SDK would lack authoritative low-level docs. missing for 10: explicit ingestion protocol/schema docs (envelope format, endpoint spec), OpenAPI/Swagger definition, independent confirmation of custom non-Sentry clients successfully sending events.", "evidenceIds": [ "glitchtip-docs-11", "glitchtip-comm-1", "glitchtip-comm-2", "glitchtip-probe-3" ] }, { "productId": "glitchtip", "storyId": "openness-api-parity", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "GlitchTip claims Sentry API compatibility, implying broad API coverage (glitchtip-docs-11), and separately offers a CLI and MCP server for programmatic/AI access (glitchtip-docs-5, glitchtip-docs-19), but there is no explicit documentation or evidence that every UI action (team/project setup, alerts, uptime monitoring config, etc.) has a corresponding API endpoint, and probes for OpenAPI/API reference docs all 404'd. Missing for 10: a published OpenAPI/API reference confirming full UI-API parity, and explicit documentation or community confirmation that all UI workflows (not just error/issue data) are API-accessible.", "evidenceIds": [ "glitchtip-docs-11", "glitchtip-docs-5", "glitchtip-docs-19", "glitchtip-probe-3" ] }, { "productId": "glitchtip", "storyId": "openness-full-export", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "GlitchTip is self-hosted and 'aims to be Sentry API compatible' (glitchtip-docs-11), implying data stays in the user's own Postgres/Redis stack and could interoperate with other Sentry-compatible tools, but there is no documented explicit 'export all data' feature, bulk export tool, or open-format export (CSV/JSON dump) in the evidence pack, and probes for docs/openapi endpoints returned 404s. Missing for 10: an explicit data-export feature/documentation, evidence of open-format bulk export, and independent confirmation that self-hosting alone satisfies full data portability.", "evidenceIds": [ "glitchtip-docs-11", "glitchtip-comm-3", "glitchtip-probe-3" ] }, { "productId": "glitchtip", "storyId": "openness-open-license", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack never mentions GlitchTip's own source code license or a public repository — the only 'open source' references are to Sentry's SDKs, not to GlitchTip's own codebase. Since the axis (whether an AI-native user can read the product's source under an open license) plainly applies to a self-hostable product like this, absence of evidence means 'none'.", "evidenceIds": [ "glitchtip-docs-1", "glitchtip-docs-26" ] }, { "productId": "glitchtip", "storyId": "openness-self-host", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "GlitchTip documents self-hosting via Docker/Docker Compose with multiple hosting recommendations (PikaPods, Elestio, Railway, etc.), and community reports corroborate this is a real, working, relatively lightweight self-hosting path (only 4 components vs Sentry's dozen) with good stability in practice. Missing for 10: no first-party hardware/scaling guidance beyond basic install docs, and one community comment notes deployment complexity ('many moving parts').", "evidenceIds": [ "glitchtip-docs-26", "glitchtip-comm-1", "glitchtip-comm-3", "glitchtip-comm-4" ] }, { "productId": "glitchtip", "storyId": "privacy-data-residency", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "GlitchTip explicitly offers a data residency option (a fully independent EU instance hosted in Frankfurt) plus self-hosting via Docker, which lets users control where data lives; a BAA is also available for compliance needs. However, evidence does not show a broader multi-region choice (e.g., US/EU/APAC selection at signup) beyond the single EU instance and self-hosting. Missing for 10: documented region selection UI/API, list of supported regions beyond EU, and independent confirmation of residency guarantees.", "evidenceIds": [ "glitchtip-docs-24", "glitchtip-docs-25", "glitchtip-docs-26" ] }, { "productId": "glitchtip", "storyId": "privacy-no-training", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "GlitchTip's self-hosting option and EU data residency (Frankfurt) give users control over where data lives, and its MCP server explicitly markets 'no data sharing required' for AI tool integration, implying data stays local. However, there is no explicit policy or documentation addressing AI model training use of customer/error data specifically, no opt-out toggle, or terms language on this topic. missing for 10: explicit AI-training data-use policy/opt-out, terms of service language on model training, independent confirmation of no training use.", "evidenceIds": [ "glitchtip-docs-9", "glitchtip-docs-19", "glitchtip-docs-24" ] }, { "productId": "glitchtip", "storyId": "privacy-retention-controls", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence pack items address data retention policies, configurable retention windows, or user-initiated deletion of stored error/event data; self-hosting and EU residency are mentioned but retention/deletion controls are never described.", "evidenceIds": [] }, { "productId": "glitchtip", "storyId": "privacy-telemetry-optout", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "GlitchTip is a self-hosted error-tracking tool; being self-hosted implies the operator controls their own data, but no evidence explicitly addresses a telemetry/usage-tracking opt-out setting for GlitchTip itself (e.g., anonymous usage stats sent to the vendor). The MCP server 'no data sharing' claim relates to AI tool data flow, not product telemetry opt-out.", "evidenceIds": [] }, { "productId": "glitchtip", "storyId": "quota-spend-controls", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack shows only trace sampling rate configuration (tracesSampleRate) as a way to reduce volume, but nothing about per-project event quotas, spend limit caps, or billing alerts to prevent surprise invoices. Given GlitchTip is a hosted/paid product with pricing plans, this axis applies, but no documentation or community evidence confirms a quota/spend-cap feature.", "evidenceIds": [ "glitchtip-docs-23", "glitchtip-docs-25" ] }, { "productId": "glitchtip", "storyId": "regression-alerts", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows GlitchTip supports release tracking (glitchtip-cli releases/deploys) and generic alerting on new issues via email/webhook, but there is no mention of a distinct 'regression' alert type that fires specifically when a previously resolved issue reappears in a newer release, as opposed to ordinary new-issue notifications.", "evidenceIds": [ "glitchtip-docs-4", "glitchtip-docs-21", "glitchtip-docs-5" ] }, { "productId": "glitchtip", "storyId": "release-health-crash-free", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "GlitchTip has releases/deployments tracking via CLI and basic performance monitoring, but there is no evidence of release health metrics like crash-free sessions/users, adoption rate per release, or canary vs stable comparison dashboards.", "evidenceIds": [] }, { "productId": "glitchtip", "storyId": "release-tracking-commits", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "GlitchTip's CLI documents creating releases and recording deployments (glitchtip-cli releases new / deploys new), which lets teams tag error events with release versions, but there is no evidence of associating commits with issues or surfacing a 'suspect commit' on an issue as Sentry does. missing for 10: commit/VCS integration for suspect-commit detection, evidence that issue pages display 'first seen in release' or commit blame, independent confirmation of this workflow.", "evidenceIds": [ "glitchtip-docs-5", "glitchtip-docs-21", "glitchtip-docs-11" ] }, { "productId": "glitchtip", "storyId": "scm-blame-context", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of repo/VCS integration (suspect commits, code owners, or stack-frame-to-source-line linking to a default branch); evidence covers source maps for readable stack traces, GitLab error tracking setup, and an MCP server for AI tools, none of which describe commit suspicion, code ownership, or jump-to-line-on-branch features.", "evidenceIds": [ "glitchtip-docs-2", "glitchtip-docs-11", "glitchtip-docs-12" ] }, { "productId": "glitchtip", "storyId": "sdk-broad-coverage", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "GlitchTip explicitly documents using Sentry's open-source SDKs for error capture and claims Sentry API compatibility, meaning any Sentry SDK (web, backend, mobile) works out of the box; community reports confirm reliable use of the Sentry SDK ecosystem across frameworks like Svelte. missing for 10: explicit mention of mobile SDK support/testing, and no independent hands-on confirmation of automatic uncaught-error capture across all three platform types (frontend/backend/mobile).", "evidenceIds": [ "glitchtip-docs-1", "glitchtip-docs-11", "glitchtip-comm-1", "glitchtip-comm-2" ] }, { "productId": "glitchtip", "storyId": "server-side-data-scrubbing", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence pack items mention PII scrubbing, server-side scrubbing rules, data-scrubbing configuration, or SDK-level before-send filtering for GlitchTip; the docs cover SDK integration, alerts, CLI, MCP, and performance tracing but never data privacy/scrubbing controls.", "evidenceIds": [] }, { "productId": "glitchtip", "storyId": "user-feedback-on-errors", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of a user feedback widget or crash-report dialog feature attached to error events; documentation covers error/performance tracking, alerts, CLI, and MCP but never mentions collecting end-user feedback tied to an event.", "evidenceIds": [] }, { "productId": "honeybadger", "storyId": "affected-users-quantified", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Evidence covers error grouping, search, deployments, alarms, and anomaly detection on error volume/rate, but nowhere quantifies user or session impact (e.g., affected-user counts) for prioritization by blast radius. missing for 10: any mention of unique users/sessions affected metric, user-impact scoring, or blast-radius prioritization feature.", "evidenceIds": [] }, { "productId": "honeybadger", "storyId": "agent-triages-errors", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Honeybadger offers a documented API for fault listing (with stack traces implied via faults/notices) and an official hosted MCP server that lets an AI assistant fetch errors, run BadgerQL queries, and manage projects, which supports pulling and triaging top issues. However, filing bugs into an external tracker is only evidenced via manual GitHub/GitLab/Jira issue export features, not shown as an MCP/agent-driven action, and there's no hands-on/independent proof of the full pull-triage-file workflow via API or MCP. missing for 10: evidence of MCP or API directly creating/filing tickets into external trackers, independent/hands-on confirmation of the end-to-end agent workflow.", "evidenceIds": [ "honeybadger-docs-13", "honeybadger-docs-19", "honeybadger-docs-30", "honeybadger-docs-23", "honeybadger-probe-3" ] }, { "productId": "honeybadger", "storyId": "agentic-agent-docs", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Honeybadger has a confirmed llms.txt endpoint (HTTP 200) plus dedicated _llms-txt reference docs (product-reference.txt, insights-reference.txt, other-reference.txt) that an agent can be pointed at, and also offers a documented MCP server as an agent-facing integration. missing for 10: independent/hands-on confirmation that an agent actually consumes llms.txt successfully, and no discoverable machine-readable OpenAPI spec to complement it.", "evidenceIds": [ "honeybadger-probe-1", "honeybadger-docs-34", "honeybadger-docs-35", "honeybadger-docs-36", "honeybadger-probe-3" ] }, { "productId": "honeybadger", "storyId": "agentic-ai-insights", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Honeybadger offers AI-adjacent capabilities: natural-language-to-query search ('Describe your search' panel), anomaly detection against learned baselines, and an MCP server that lets an external AI assistant query BadgerQL/Insights data and help investigate issues. However, these are either query-assist/statistical alerting features rather than generative insight/suggestion output embedded directly in the product UI, or they require connecting an external AI agent via MCP rather than native in-product AI-generated insights. missing for 10: evidence of in-app AI-generated summaries/root-cause suggestions surfaced directly in the Honeybadger UI (not via external MCP client), and independent/hands-on validation of the natural-language search or anomaly detection quality.", "evidenceIds": [ "honeybadger-docs-5", "honeybadger-docs-9", "honeybadger-docs-13", "honeybadger-docs-30", "honeybadger-probe-3" ] }, { "productId": "honeybadger", "storyId": "agentic-autonomous-automation", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Honeybadger ships several always-on background automations — Alarms (BadgerQL query + threshold triggers), anomaly detection against a learned baseline, check-ins for cron/scheduled jobs, and uptime monitoring — that run autonomously once configured. However, these are traditional monitoring rules rather than AI-agent-driven automations, and the MCP integration is scoped to investigation/query capabilities rather than configuring or running autonomous background workflows. Missing for 10: evidence that an AI agent can itself create/manage these background automations (e.g. via MCP or API), and any AI-specific autonomous workflow/task runner beyond monitoring alerts.", "evidenceIds": [ "honeybadger-docs-8", "honeybadger-docs-9", "honeybadger-docs-37", "honeybadger-docs-38", "honeybadger-docs-13", "honeybadger-docs-30" ] }, { "productId": "honeybadger", "storyId": "agentic-builtin-assistant", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Honeybadger offers a narrow built-in AI feature — a 'Describe your search' panel that turns natural-language descriptions into BadgerQL queries — but this is a single-purpose assistant, not a general-purpose in-product AI agent you can delegate broader tasks to. The more prominent AI story here (MCP server, docs-13/30) is the reverse direction: it lets an external AI assistant (e.g., Claude) connect to Honeybadger, not a built-in assistant inside Honeybadger itself. Missing for 10: a general-purpose in-app AI assistant capable of multi-step task delegation, evidence of broader assistant capabilities beyond search-query generation, and independent/hands-on corroboration of this feature's usefulness.", "evidenceIds": [ "honeybadger-docs-5", "honeybadger-docs-13", "honeybadger-docs-30" ] }, { "productId": "honeybadger", "storyId": "agentic-headless", "verdict": "full", "quality": 6, "confidence": "medium", "rationale": "Honeybadger ships a CLI and REST API (e.g., `honeybadger deploy`, exceptions/deployments/source-map endpoints) explicitly designed for scripted, non-interactive use, which is exactly what's needed to invoke it headlessly from CI pipelines, and error/deploy tracking runs automatically once configured. Missing for 10: explicit CI/CD integration examples (e.g., GitHub Actions/GitLab CI snippets) and independent hands-on confirmation of CI usage.", "evidenceIds": [ "honeybadger-docs-18", "honeybadger-docs-31", "honeybadger-docs-17", "honeybadger-docs-15", "honeybadger-docs-1", "honeybadger-probe-4" ] }, { "productId": "honeybadger", "storyId": "agentic-mcp-client", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Honeybadger is an error-tracking/monitoring SaaS, not an AI agent or assistant that itself consumes tools via MCP; the evidence shows Honeybadger exposes an MCP *server* so external AI assistants can use Honeybadger's tools (docs-13, docs-14, docs-30, probe-3), which is the opposite role from 'plugging MCP servers into this product.' There is no agentic component in Honeybadger designed to act as an MCP client, so this axis is a category error for this product.", "evidenceIds": [ "honeybadger-docs-13", "honeybadger-docs-14", "honeybadger-docs-30", "honeybadger-probe-3" ] }, { "productId": "honeybadger", "storyId": "agentic-mcp-server", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "Honeybadger ships an official hosted MCP server (docs.honeybadger.io/resources/mcp/) that lets an AI assistant connect and gain project management, error investigation, and Insights/BadgerQL query capabilities, with OAuth-based connection flow documented in the changelog. This is first-party documentation of exactly the described capability, though missing for 10: independent/hands-on verification of the MCP server working in practice.", "evidenceIds": [ "honeybadger-docs-13", "honeybadger-docs-14", "honeybadger-docs-30", "honeybadger-probe-3" ] }, { "productId": "honeybadger", "storyId": "agentic-nl-commands", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Honeybadger supports natural-language search via a 'Describe your search' panel that translates plain-language descriptions into BadgerQL queries, and its MCP server lets a connected AI assistant fetch data, run BadgerQL queries, and investigate issues conversationally. However, most core operations (deployments, alarms, integrations, CLI) still rely on structured queries/commands rather than direct natural-language control, and there's no independent evidence of how robust the NL search feature is in practice. Missing for 10: broader native NL command support across all product areas, independent/hands-on validation of NL search accuracy.", "evidenceIds": [ "honeybadger-docs-5", "honeybadger-docs-13", "honeybadger-docs-30", "honeybadger-probe-3" ] }, { "productId": "honeybadger", "storyId": "agentic-official-cli", "verdict": "full", "quality": 6, "confidence": "medium", "rationale": "Honeybadger ships an official CLI (Ruby gem) documented with commands like `honeybadger deploy` for notifying deployments and other utilities, confirmed by docs and probe. It exists as a first-party tool a script or AI agent could invoke, though it is narrow in scope (deployment notification, limited to Ruby ecosystem) and not marketed as AI-native. Missing for 10: broader multi-language CLI coverage, AI-specific CLI features/flags, and independent/hands-on confirmation of agentic usage.", "evidenceIds": [ "honeybadger-docs-18", "honeybadger-docs-31", "honeybadger-probe-4" ] }, { "productId": "honeybadger", "storyId": "agentic-public-api", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Honeybadger exposes a well-documented public REST API for reporting exceptions, source maps, deployments, and querying faults (docs-15,16,17,19,36), plus a CLI (docs-18,31) and MCP server (docs-13,30) enabling AI agents to interact with it programmatically. No formal OpenAPI/Swagger spec was found via probe, slightly limiting machine-readable discoverability. Missing for 10: a discoverable OpenAPI/Swagger spec and independent third-party confirmation of API robustness.", "evidenceIds": [ "honeybadger-docs-15", "honeybadger-docs-16", "honeybadger-docs-17", "honeybadger-docs-19", "honeybadger-docs-18", "honeybadger-docs-31", "honeybadger-docs-13", "honeybadger-docs-30", "honeybadger-probe-2", "honeybadger-probe-3", "honeybadger-probe-4" ] }, { "productId": "honeybadger", "storyId": "agentic-scoped-keys", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Honeybadger's hosted MCP server supports OAuth-based approval flows for connecting an agent, which implies some access-control mechanism, but there is no documentation of issuing scoped or least-privilege API keys (e.g., read-only, project-restricted) specifically for agent use. missing for 10: explicit scoped/least-privilege API key creation, granular permission levels for agent credentials, documentation of restricting MCP/agent access to specific resources.", "evidenceIds": [ "honeybadger-docs-13", "honeybadger-docs-14", "honeybadger-probe-3" ] }, { "productId": "honeybadger", "storyId": "agentic-sdks", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Honeybadger documents official client libraries/SDKs for multiple languages (Ruby, JS, Elixir/Oban) plus a public reporting/exceptions API, source-map API, and a Ruby CLI, giving AI-native builders concrete integration points (honeybadger-docs-1, -16, -24, -33, -18, -36). However there's no formal OpenAPI/swagger spec (404s on all candidate paths, honeybadger-probe-2) and no consolidated multi-language SDK catalog or independent hands-on confirmation of building against these SDKs. Missing for 10: a machine-readable API spec (OpenAPI), broader documented SDK coverage across major languages, and independent/community validation of SDK integration quality.", "evidenceIds": [ "honeybadger-docs-1", "honeybadger-docs-16", "honeybadger-docs-18", "honeybadger-docs-24", "honeybadger-docs-33", "honeybadger-docs-36", "honeybadger-probe-2" ] }, { "productId": "honeybadger", "storyId": "agentic-webhooks", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Evidence confirms Honeybadger sends webhook requests (docs-25 mentions 'web hooks' alongside source maps and uptime checks with a secret token header), implying a webhook subscription mechanism exists, but no documentation details how to configure webhook endpoints, which events trigger them, or payload formats. Missing for 10: dedicated webhook setup/configuration docs, list of subscribable event types, payload schema examples, and independent confirmation of reliability.", "evidenceIds": [ "honeybadger-docs-25" ] }, { "productId": "honeybadger", "storyId": "ai-fix-pull-request", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Honeybadger's MCP and integrations let an AI assistant investigate errors and query data, but there is no evidence of it drafting a code fix or opening a pull request against a repo. Missing for 10: any capability or documentation of AI-generated code fixes, automated PR creation, or git-write integration.", "evidenceIds": [ "honeybadger-docs-13", "honeybadger-docs-30", "honeybadger-docs-23" ] }, { "productId": "honeybadger", "storyId": "ai-root-cause", "verdict": "partial", "quality": 4, "confidence": "medium", "rationale": "Honeybadger's MCP server lets a connected AI assistant fetch error data, run BadgerQL queries, and 'analyze patterns to help investigate production issues' (docs-13, docs-30), which comes close to AI-driven root-cause analysis. However, this requires the user's own external AI client via MCP rather than a built-in platform AI that automatically correlates stack traces, breadcrumbs, and commits into a proposed root cause — no evidence of an integrated automatic root-cause feature exists. Missing for 10: evidence of a first-party AI feature (not just MCP data access) that ingests stack trace + breadcrumbs + commit history and outputs a concrete root-cause hypothesis, and any hands-on/independent validation of accuracy.", "evidenceIds": [ "honeybadger-docs-13", "honeybadger-docs-30", "honeybadger-probe-3" ] }, { "productId": "honeybadger", "storyId": "alert-routing-channels", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Honeybadger docs show Slack and PagerDuty integrations, plus 'Alarms' that combine a BadgerQL query with a threshold (e.g., count > N) and anomaly detection for volume spikes, which covers frequency-spike-style alerting and routing to chat/on-call tools. However, there's no evidence of explicit per-project/per-environment routing rules, no documented email alert channel, and no dedicated 'affected-user count' threshold type distinct from generic query-based alarms — missing for 10: environment-based routing rules, email as a routing target, explicit affected-user-count threshold configuration, and severity-tiered rule matrices.", "evidenceIds": [ "honeybadger-docs-8", "honeybadger-docs-9", "honeybadger-docs-10", "honeybadger-docs-12" ] }, { "productId": "honeybadger", "storyId": "api-interactive-docs", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Docs describe static API endpoints (reporting-exceptions, faults, source maps) but there is no evidence of an interactive, runnable API reference/explorer; a probe explicitly found no OpenAPI/Swagger spec at any candidate URL (404s), indicating no such interactive documentation exists.", "evidenceIds": [ "honeybadger-probe-2", "honeybadger-docs-15", "honeybadger-docs-19" ] }, { "productId": "honeybadger", "storyId": "api-machine-spec", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The API is documented in prose (endpoints for reporting exceptions, faults, deployments, source maps), but a direct probe for machine-readable OpenAPI/Swagger specs at standard locations returned 404 across all candidates, and no evidence pack item links to a downloadable spec file. missing for 10: an actual OpenAPI/Swagger JSON or YAML file, any documented spec download link, or generator tooling.", "evidenceIds": [ "honeybadger-probe-2", "honeybadger-docs-19", "honeybadger-docs-15" ] }, { "productId": "honeybadger", "storyId": "api-sandbox", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Honeybadger is an error-tracking/monitoring SaaS, not a sandbox/test-environment tool; the evidence pack contains no concept of sandbox environments or test-vs-production data isolation for testing purposes, making this axis a category error for this product type.", "evidenceIds": [] }, { "productId": "honeybadger", "storyId": "api-versioning-policy", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows a documented REST API (v1/notices, faults, source maps, deployments) but no mention of API versioning scheme or a deprecation policy; the openapi.json probe returned 404s, suggesting no formal API spec is published. No evidence of version headers, changelog of breaking API changes, or deprecation timelines.", "evidenceIds": [ "honeybadger-docs-15", "honeybadger-docs-19", "honeybadger-probe-2" ] }, { "productId": "honeybadger", "storyId": "automation-bulk-operations", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "There's some evidence of bulk-like operations: deployments automatically mark all unresolved errors as resolved at once (docs-6, docs-29), and BadgerQL/Insights let users run aggregations and calculations across many events (docs-35, docs-8). However, there's no documented UI or API feature for explicit multi-select bulk actions (e.g., bulk resolve/delete/tag many errors at once) beyond the automatic deploy-triggered resolution. Missing for 10: explicit bulk-select UI/API endpoints for mass error management, documented batch operations beyond auto-resolve-on-deploy, independent confirmation of bulk workflows.", "evidenceIds": [ "honeybadger-docs-6", "honeybadger-docs-29", "honeybadger-docs-35", "honeybadger-docs-8" ] }, { "productId": "honeybadger", "storyId": "automation-rules-engine", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Honeybadger supports rule-like automation: Alarms combine a BadgerQL query with a threshold to trigger alerts automatically (docs-8), anomaly detection triggers alerts when error volume deviates from baseline (docs-9), and deployments automatically resolve open errors and notify teams (docs-6, docs-29). These qualify as user-defined rules triggering automatic actions on events, but there's no evidence of a general-purpose automation/workflow engine (e.g., custom actions beyond alert/resolve/notify) or that AI-native users can programmatically create these rules via API/CLI/MCP. missing for 10: broader custom-action rule engine, API/CLI/MCP support for creating or managing alarms/anomaly rules, independent verification of rule automation working in practice.", "evidenceIds": [ "honeybadger-docs-8", "honeybadger-docs-9", "honeybadger-docs-6", "honeybadger-docs-29" ] }, { "productId": "honeybadger", "storyId": "automation-scheduled-jobs", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Honeybadger is an error-tracking/APM/uptime-monitoring product; its 'Check-ins' feature only monitors pings from cron jobs that are scheduled elsewhere, it does not provide a scheduling engine for recurring jobs or workflows. Scheduling automation is outside this product's category, making the axis inapplicable rather than unmet.", "evidenceIds": [ "honeybadger-docs-37" ] }, { "productId": "honeybadger", "storyId": "automation-versioned-workflows", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Honeybadger is an error-tracking/monitoring product, not an automation-builder; there is no concept of 'automations' to version, review, or roll back—this axis targets workflow/automation platforms, not APM tools.", "evidenceIds": [] }, { "productId": "honeybadger", "storyId": "breadcrumbs-custom-context", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Evidence shows tag-based search syntax (implying tags can be attached) and a changelog note about 'event context' and 'event correlation' configuration in JS packages, but there is no explicit documentation of breadcrumb trails or explicit user-context attachment on error notices. missing for 10: explicit breadcrumb API/docs, explicit user context attachment API, worked example showing tags+context+breadcrumbs together on a single event.", "evidenceIds": [ "honeybadger-docs-4", "honeybadger-docs-33", "honeybadger-docs-2" ] }, { "productId": "honeybadger", "storyId": "error-grouping-dedup", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Docs confirm automatic grouping of identical errors into a single issue with navigation between occurrences, and explicit customization via error class, component, stack trace, or custom fingerprint; community feedback corroborates satisfaction with deduping. Missing for 10: independent/hands-on evidence of the fingerprinting algorithm's accuracy at scale (thousands of events) and real-world examples of re-grouping mistakes being fixed.", "evidenceIds": [ "honeybadger-docs-2", "honeybadger-docs-3", "honeybadger-comm-1" ] }, { "productId": "honeybadger", "storyId": "error-trend-dashboards", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Honeybadger's BadgerQL/Insights, deployment tracking (correlating errors with releases, 'occurred.after:last deploy' queries), and anomaly detection support volume-over-time and per-release regression tracking, giving engineering leaders solid analytics tools. However, evidence never shows a native cross-project/cross-team rollup dashboard — only per-account switching (docs-34) — so the 'across projects and teams' aggregation aspect of the story is unproven. Missing for 10: explicit cross-project/team aggregated dashboard UI, named 'top regressions' report, and independent/hands-on confirmation of dashboard usability at leadership level.", "evidenceIds": [ "honeybadger-docs-8", "honeybadger-docs-9", "honeybadger-docs-20", "honeybadger-docs-29", "honeybadger-docs-26", "honeybadger-docs-35", "honeybadger-docs-34" ] }, { "productId": "honeybadger", "storyId": "event-search-queries", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Honeybadger documents a real BadgerQL-style query syntax (tag:, class:, component:, action:, occurred.after: 'last deploy', negation with -tag:) for filtering errors/events by tag, release/deploy, environment-like properties and custom fields, plus a natural-language-to-query assist feature and Insights query capabilities for aggregation/filtering across fields. missing for 10: explicit documentation of cross-project search/filtering (queries appear scoped per-project) and independent hands-on validation of the query syntax's breadth beyond docs.", "evidenceIds": [ "honeybadger-docs-4", "honeybadger-docs-5", "honeybadger-docs-20", "honeybadger-docs-26", "honeybadger-docs-27", "honeybadger-docs-35" ] }, { "productId": "honeybadger", "storyId": "issue-tracker-sync", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Honeybadger documents issue export/linking to GitHub, GitLab, and Jira (including full Jira wiki markup), and auto-resolves errors on deploy, but there is no evidence of Linear integration nor explicit two-way status sync where resolving/reopening the external ticket automatically resolves/reopens the Honeybadger error (only Slack shows resolve/reopen buttons within Honeybadger itself). missing for 10: Linear integration, documented two-way sync (ticket resolution reopening/closing the Honeybadger error automatically), independent confirmation of sync behavior.", "evidenceIds": [ "honeybadger-docs-23", "honeybadger-docs-6", "honeybadger-docs-10" ] }, { "productId": "honeybadger", "storyId": "issue-triage-workflow", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Honeybadger documents resolving and reopening errors (via Slack integration button) and auto-resolving errors on deploy, plus fault listing via API, which covers part of the triage workflow. However, there's no direct evidence of 'assign an owner', 'ignore', or 'snooze' actions, nor confirmation that a resolved issue is automatically reopened when it regresses (only manual reopen via Slack is shown). missing for 10: assign-owner action, ignore action, snooze action, automatic reopening on regression evidence.", "evidenceIds": [ "honeybadger-docs-10", "honeybadger-docs-6", "honeybadger-docs-29", "honeybadger-docs-19" ] }, { "productId": "honeybadger", "storyId": "issues-api-read", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Honeybadger documents a REST API for faults (issues) with list/single-fault retrieval and search-style querying (docs-19, docs-4), plus a reporting API for creating notices, but there is no evidence of an update-issue endpoint, a raw-events fetch endpoint, or explicit scoped-token support (only a generic API key/Honeybadger-Token is mentioned). missing for 10: documented issue-update endpoint, raw event retrieval endpoint, explicit scoped/read-only token documentation.", "evidenceIds": [ "honeybadger-docs-19", "honeybadger-docs-4", "honeybadger-docs-15", "honeybadger-docs-25" ] }, { "productId": "honeybadger", "storyId": "js-sourcemap-unminify", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Honeybadger documents a Source Map Upload API for un-minifying JS stack traces (honeybadger-docs-16, honeybadger-docs-28), and it has a general CLI (honeybadger-docs-18, honeybadger-probe-4) with deploy notification support, but the evidence never explicitly shows a CI-oriented source map upload command in that CLI or a bundler plugin (webpack/rollup/vite) for automated CI upload. missing for 10: explicit CLI subcommand or bundler plugin for source map upload, CI usage examples/integration guide, independent confirmation of correct symbolication in production.", "evidenceIds": [ "honeybadger-docs-16", "honeybadger-docs-28", "honeybadger-docs-18", "honeybadger-probe-4" ] }, { "productId": "honeybadger", "storyId": "merge-split-issues", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Honeybadger documents customizable grouping via custom fingerprints, error class, component, or stack trace, which lets developers effectively split incorrectly-collapsed errors into distinct issues [honeybadger-docs-2][honeybadger-docs-3]. However, there is no documented UI or API action for merging two separately-grouped issues back into one bug — the evidence only covers grouping-at-ingestion via fingerprint customization, not a manual merge workflow. Missing for 10: explicit merge-issues feature/API, evidence of an UI 'merge' action, and independent confirmation that fingerprint changes retroactively re-group existing occurrences.", "evidenceIds": [ "honeybadger-docs-2", "honeybadger-docs-3", "honeybadger-docs-26" ] }, { "productId": "honeybadger", "storyId": "mobile-crash-reporting", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Evidence pack covers Ruby, JS, and general error-reporting APIs, deployments, integrations, and MCP/CLI tooling, but contains no mention of iOS, Android, or NDK SDKs, or native mobile crash context (device, OS, app version). Missing for 10: iOS SDK docs, Android SDK docs, NDK/native crash capture, mobile device/app-version context capture.", "evidenceIds": [ "honeybadger-docs-1", "honeybadger-docs-15", "honeybadger-docs-33" ] }, { "productId": "honeybadger", "storyId": "native-symbolication", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Evidence shows Honeybadger supports JavaScript source map upload/unminification (docs-16, docs-28) but contains no mention of dSYM, ProGuard, or native mobile debug symbol upload/symbolication anywhere in the pack.", "evidenceIds": [ "honeybadger-docs-16", "honeybadger-docs-28" ] }, { "productId": "honeybadger", "storyId": "noise-spike-controls", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Honeybadger has real alert-fatigue tooling—error deduping/grouping, anomaly detection against a learned baseline, BadgerQL-based alarms with thresholds, and auto-resolve on deploy plus Slack resolve/reopen—but none of the evidence names the specific mechanisms in the story: spike protection, per-key rate limiting, or explicit mute/ignore/snooze rules for notifications. missing for 10: explicit spike-protection throttling, per-key/per-error rate limits, and a documented mute/ignore/snooze feature for alerts.", "evidenceIds": [ "honeybadger-docs-3", "honeybadger-docs-8", "honeybadger-docs-9", "honeybadger-docs-10", "honeybadger-docs-6", "honeybadger-docs-29" ] }, { "productId": "honeybadger", "storyId": "open-ingest-protocol", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Honeybadger documents a plain REST endpoint (POST JSON to https://api.honeybadger.io/v1/notices) for reporting exceptions, plus related endpoints for source maps and deployments, meaning any custom client can integrate without the official SDK. However, there's no formal OpenAPI/schema spec (probe confirms 404s) and no independent evidence of third-party/community SDKs actually built against this raw API. Missing for 10: published OpenAPI/schema definition, independent examples of non-official clients successfully using the ingestion endpoint.", "evidenceIds": [ "honeybadger-docs-15", "honeybadger-docs-36", "honeybadger-docs-16", "honeybadger-docs-17", "honeybadger-probe-2" ] }, { "productId": "honeybadger", "storyId": "openness-api-parity", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Honeybadger exposes a documented REST API for core actions—reporting exceptions, deployments, source maps, and listing faults (honeybadger-docs-15,17,19,28)—plus a CLI and MCP server for agent access (honeybadger-docs-18,13,31). However, the probe found no OpenAPI/swagger spec (honeybadger-probe-2), and many UI-only capabilities (resolving/reopening errors, BadgerQL alarm configuration, team/SAML management, account switching) are not shown to have API equivalents. Missing for 10: comprehensive API parity for error resolution, alarms, account/team management, and a published OpenAPI spec confirming full coverage.", "evidenceIds": [ "honeybadger-docs-15", "honeybadger-docs-17", "honeybadger-docs-19", "honeybadger-docs-28", "honeybadger-docs-18", "honeybadger-docs-13", "honeybadger-probe-2", "honeybadger-docs-34" ] }, { "productId": "honeybadger", "storyId": "openness-full-export", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Honeybadger exposes a REST API (e.g. list/get faults, reporting exceptions) that returns data in JSON, which could be used to script an export of error data, but there is no documented bulk 'export all your account data' feature, no GDPR-style data-portability tool, and no mention of full account deletion/export workflow. missing for 10: dedicated full-account data export feature, documented open-format bulk export tool, evidence of complete data portability/leave workflow.", "evidenceIds": [ "honeybadger-docs-19", "honeybadger-docs-15", "honeybadger-docs-23" ] }, { "productId": "honeybadger", "storyId": "openness-open-license", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Honeybadger is a closed, commercial SaaS product; no evidence in the pack indicates its source code is published under an open license (client libraries being open-source is not addressed, and the core service is proprietary). Missing for 10: any mention of an open-source license, public source repository, or licensing terms.", "evidenceIds": [] }, { "productId": "honeybadger", "storyId": "openness-self-host", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Honeybadger is presented as a hosted SaaS product (api.honeybadger.io) with no evidence of an open-source self-hosted core, on-premise deployment option, or Docker/self-host installation guide anywhere in the evidence pack.", "evidenceIds": [] }, { "productId": "honeybadger", "storyId": "privacy-data-residency", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack addresses data residency, regional hosting options, or ability to choose where data is stored; only data retention (Insights replication) and security token handling are mentioned. missing for 10: any mention of region selection, EU/US hosting options, or data residency controls.", "evidenceIds": [] }, { "productId": "honeybadger", "storyId": "privacy-no-training", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack addresses AI training data usage or opt-out policies for Honeybadger's data; the product is an error-tracking/monitoring tool and this axis is plausible (e.g., a privacy policy statement about AI training) but nothing documents it. Missing for 10: any privacy policy statement on AI training data usage, opt-out mechanism, or data processing terms addressing AI model training.", "evidenceIds": [] }, { "productId": "honeybadger", "storyId": "privacy-retention-controls", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence only hints that Business/Enterprise accounts can replicate Insights data for longer retention (docs-22), but there is no mention of any user-facing control to configure retention windows, purge/delete data on demand, or comply with deletion requests. Missing for 10: documented retention settings, a data-deletion API/UI, and any GDPR-style erasure mechanism.", "evidenceIds": [ "honeybadger-docs-22" ] }, { "productId": "honeybadger", "storyId": "privacy-telemetry-optout", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "Honeybadger itself is an error-tracking/telemetry product whose core function is collecting data from customer apps; the evidence pack contains no mention of an opt-out mechanism for telemetry/usage tracking related to the AI assistant/MCP integration or the product's own data collection practices. Since this axis (opting out of tracking) is a fair privacy-posture question for an AI-native user of any product, but no evidence shows such an opt-out exists, the verdict is none. Missing for 10: any documentation of an opt-out/telemetry-disable setting, privacy controls for AI assistant usage data, or opt-out options for the MCP integration.", "evidenceIds": [] }, { "productId": "honeybadger", "storyId": "quota-spend-controls", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence anywhere in the pack of event quota caps, spend/budget limits, or usage-based throttling controls for a project; the evidence covers error grouping, search, deployments, alarms, and MCP/CLI integrations but nothing about cost or quota governance.", "evidenceIds": [] }, { "productId": "honeybadger", "storyId": "regression-alerts", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Honeybadger auto-resolves errors on deploy and tracks reoccurrences (doc-6, doc-29), and Slack messages include a 'resolve or reopen' button implying errors can reopen (doc-10), which is the mechanical basis for detecting a fixed error's return. However, there is no explicit evidence of a distinct 'regression' alert type or notification that flags a reopened error differently from a brand-new issue, so SREs would have to infer regressions from reopen state rather than get a dedicated alert. Missing for 10: explicit regression-specific alert/notification distinct from new-issue alerts, and confirmation that reopening a resolved error triggers a notification at all.", "evidenceIds": [ "honeybadger-docs-6", "honeybadger-docs-29", "honeybadger-docs-10", "honeybadger-docs-20" ] }, { "productId": "honeybadger", "storyId": "release-health-crash-free", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Honeybadger documents deployment tracking (correlating errors with deploys, diffing against last deploy, auto-resolving errors on deploy) but there is no evidence of crash-free sessions/users metrics or per-release adoption percentages, which are the specific release-health metrics this story asks about.", "evidenceIds": [ "honeybadger-docs-6", "honeybadger-docs-7", "honeybadger-docs-17", "honeybadger-docs-20", "honeybadger-docs-29" ] }, { "productId": "honeybadger", "storyId": "release-tracking-commits", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Honeybadger's deployment tracking records deploys, auto-resolves errors on deploy, and (with GitHub/GitLab connected) links a revision to a diff of what changed since the last deploy, plus supports queries like 'occurred.after:last deploy' to correlate errors with releases. However, there's no documented evidence of a per-issue 'first seen in release X' badge or automatic 'suspect commit' attribution pinpointing which commit likely caused a specific error, which is the core of this story. Missing for 10: explicit per-error 'first appeared in release' metadata, automated suspect-commit/blame attribution shown on the issue itself, independent corroboration of this workflow.", "evidenceIds": [ "honeybadger-docs-6", "honeybadger-docs-7", "honeybadger-docs-20", "honeybadger-docs-29" ] }, { "productId": "honeybadger", "storyId": "scm-blame-context", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows Honeybadger connects to GitHub/GitLab for deploy diffs and issue exports (docs-7, docs-23), but there is no evidence of suspect-commit identification, code-owner attribution, or jumping from a stack frame to the exact line on the default branch in the repo. Missing for 10: suspect commit detection, code owner mapping, stack-frame-to-source-line deep linking.", "evidenceIds": [ "honeybadger-docs-7", "honeybadger-docs-23" ] }, { "productId": "honeybadger", "storyId": "sdk-broad-coverage", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Docs confirm automatic uncaught-error capture via client libraries (\"errors thrown by your application are automatically sent to our API\"), JS/frontend automatic instrumentation and source-map support, and a generic exceptions API for backend reporting. However, the evidence pack lacks explicit confirmation of dedicated mobile SDKs (iOS/Android) or a list of officially supported backend languages beyond the Ruby CLI/gem reference, so mobile coverage is unconfirmed. Missing for 10: explicit mobile SDK documentation, multi-language backend SDK list, independent hands-on verification of auto-capture across platforms.", "evidenceIds": [ "honeybadger-docs-1", "honeybadger-docs-16", "honeybadger-docs-28", "honeybadger-docs-33", "honeybadger-docs-36", "honeybadger-docs-15" ] }, { "productId": "honeybadger", "storyId": "server-side-data-scrubbing", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence pack items mention PII scrubbing, data filtering, or SDK-level/server-side redaction rules — none of the docs, changelog, or community items reference this capability at all. missing for 10: server-side scrubbing rule configuration, SDK filter_keys/params filtering, any mention of PII/sensitive data handling before persistence.", "evidenceIds": [] }, { "productId": "honeybadger", "storyId": "user-feedback-on-errors", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Honeybadger's evidence covers automatic error capture, grouping, search, deployments, and integrations, but there is no mention of a user-facing crash-report dialog or feedback widget that lets end-users submit feedback tied to a specific error occurrence.", "evidenceIds": [] }, { "productId": "raygun", "storyId": "affected-users-quantified", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Raygun explicitly supports ordering/prioritizing errors by 'users affected' and occurrence counts, which directly maps to blast-radius prioritization rather than raw event counts (raygun-docs-3). However, there's no evidence of session-level impact quantification, percentage-of-userbase metrics, or dedicated 'impact score' analytics beyond simple affected-user counts. Missing for 10: session-affected metrics, quantified blast-radius scoring/percentage-of-users, independent corroboration of this prioritization workflow in practice.", "evidenceIds": [ "raygun-docs-3" ] }, { "productId": "raygun", "storyId": "agent-triages-errors", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Raygun does have an official MCP server (raygun-probe-3) and docs describe rich stack-trace/error data (raygun-docs-1) plus a triage workflow via error statuses (raygun-docs-25, raygun-docs-10) and repo integrations (raygun-docs-2), but there is no evidence of an actual 'file bug into external tracker' action via API/MCP, and the lack of a public OpenAPI spec or llms.txt (raygun-probe-1, raygun-probe-2) undercuts confidence in a full agent-friendly API surface. missing for 10: evidence of automated ticket creation in external trackers via API/MCP, a documented OpenAPI/agent-facing API spec, and hands-on proof the MCP server can retrieve top production issues end-to-end.", "evidenceIds": [ "raygun-probe-3", "raygun-docs-1", "raygun-docs-25", "raygun-docs-2", "raygun-probe-1", "raygun-probe-2" ] }, { "productId": "raygun", "storyId": "agentic-agent-docs", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "A direct probe confirms llms.txt returns 404, and no evidence mentions agent-oriented documentation formats for Raygun; the only agent-related evidence is an MCP server, which is a separate axis.", "evidenceIds": [ "raygun-probe-1" ] }, { "productId": "raygun", "storyId": "agentic-ai-insights", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Raygun's AI Resolution feature (raygun-docs-4, raygun-docs-5, raygun-docs-27) provides AI-powered suggestions on root cause and fixes for errors directly inside the crash reporting product, powered by OpenAI/Azure OpenAI. However, this is scoped narrowly to crash/error resolution rather than broad AI-generated insights across all data (APM, RUM, etc.), and there's no independent/hands-on corroboration of its effectiveness. Missing for 10: evidence of AI insights across other Raygun modules (APM, RUM), independent user validation of AI suggestion quality.", "evidenceIds": [ "raygun-docs-4", "raygun-docs-5", "raygun-docs-27" ] }, { "productId": "raygun", "storyId": "agentic-autonomous-automation", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Raygun is a monitoring/error-tracking product with alerting rules (ignore conditions, notification integrations) and an AI-assisted resolution chat, but there is no evidence of autonomous background automations/agents that run tasks independently—AI resolution appears to be an interactive chat tool, not an autonomous agent. Missing for 10: evidence of scheduled/triggered autonomous workflows, agentic task execution without human prompting, or any 'automation' feature beyond notification rules.", "evidenceIds": [ "raygun-docs-4", "raygun-docs-5", "raygun-docs-27", "raygun-docs-11", "raygun-docs-17" ] }, { "productId": "raygun", "storyId": "agentic-builtin-assistant", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Raygun ships a built-in 'AI Resolution' chat assistant (powered by OpenAI/Azure OpenAI) that users can converse with to diagnose root causes and get fix suggestions for crash errors, with collaborative chat and comment-linking features. However, this AI delegation is scoped narrowly to crash/error resolution rather than general task delegation across the product, and there's no independent/hands-on verification of its effectiveness. Missing for 10: evidence of AI assistant use beyond crash resolution (e.g., APM, RUM), independent user reports validating its usefulness.", "evidenceIds": [ "raygun-docs-4", "raygun-docs-5", "raygun-docs-6", "raygun-docs-27" ] }, { "productId": "raygun", "storyId": "agentic-headless", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Raygun exposes an external access token used by CI/CD pipelines for the Deployments API, dSYM/ProGuard/JS source map uploads, which supports some headless automation in build pipelines, and an export API for CSV data. However there is no documented CLI, no evidence of a general headless SDK/runner for CI-based testing, and API/OpenAPI probes returned 404s. missing for 10: dedicated CLI or SDK for full headless CI operation, documented API reference beyond token-based uploads, independent evidence of CI usage.", "evidenceIds": [ "raygun-docs-8", "raygun-docs-23", "raygun-docs-9", "raygun-probe-2" ] }, { "productId": "raygun", "storyId": "agentic-mcp-client", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "Evidence only shows Raygun publishing its own MCP server (raygun-probe-3) so external agents can call Raygun's tools — the reverse of what this story asks (Raygun consuming external MCP servers as a client). No documentation or product feature shows Raygun's AI Resolution chat or any other component accepting/plugging in third-party MCP servers.", "evidenceIds": [ "raygun-probe-3", "raygun-docs-5" ] }, { "productId": "raygun", "storyId": "agentic-mcp-server", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "There is evidence of an official MCP server repo (MindscapeHQ/mcp-server-raygun) enabling agent connection, but no first-party documentation, setup guide, or independent corroboration confirming its official status or maturity. Missing for 10: first-party docs referencing the MCP server, usage/setup documentation, independent hands-on validation.", "evidenceIds": [ "raygun-probe-3" ] }, { "productId": "raygun", "storyId": "agentic-nl-commands", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Raygun ships an official MCP server (raygun-probe-3) which would let AI agents interact with Raygun data via natural-language-driven tool calls, and it has an AI chat feature for error resolution (raygun-docs-4/5/27) where users converse in natural language about a specific error. However, there is no evidence of natural-language commands for broader product operation (e.g., managing settings, triaging errors, running queries) beyond this narrow AI-resolution chat and the MCP server's undocumented scope. Missing for 10: documented list of MCP tools/actions, evidence of natural-language control over core workflows (search, filtering, ignoring, exporting), and any independent/hands-on confirmation.", "evidenceIds": [ "raygun-docs-4", "raygun-docs-5", "raygun-docs-27", "raygun-probe-3" ] }, { "productId": "raygun", "storyId": "agentic-official-cli", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of an official Raygun CLI tool for AI-native workflows; docs only mention API tokens, dashboard exports, and integrations, and probes show no CLI or openapi/llms.txt artifacts confirming CLI presence.", "evidenceIds": [] }, { "productId": "raygun", "storyId": "agentic-public-api", "verdict": "partial", "quality": 4, "confidence": "medium", "rationale": "Raygun docs reference a public API (Deployments API, JS source-map upload API, external access tokens for various services) implying programmatic access exists, and a third-party MCP server exists for tool integration. However, no dedicated API reference or OpenAPI/Swagger spec was found (probes for openapi.json/llms.txt returned 404s), so the API's documentation depth for AI-native automation is unclear. Missing for 10: a full public API reference/spec, first-party API docs page, and confirmation of comprehensive endpoint coverage beyond token-based uploads.", "evidenceIds": [ "raygun-docs-8", "raygun-docs-15", "raygun-probe-2", "raygun-probe-3" ] }, { "productId": "raygun", "storyId": "agentic-scoped-keys", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Raygun documents a single 'external access token' for various API uses (raygun-docs-8, raygun-docs-23) but no evidence of scoped, least-privilege, or agent-specific API credentials/roles. No mention of granular permission scopes for the MCP server (raygun-probe-3) or API keys either.", "evidenceIds": [ "raygun-docs-8", "raygun-docs-23", "raygun-probe-3" ] }, { "productId": "raygun", "storyId": "agentic-sdks", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Evidence shows Raygun has language-specific integrations (JavaScript source maps, .NET client per community reports) and an external API access token for various services, implying SDKs exist, but there is no dedicated, comprehensive official SDK documentation set, package repository links, or coverage across multiple languages presented in the evidence pack. Missing for 10: explicit official SDK reference docs, GitHub/npm/nuget package listings, multi-language coverage details, and independent verification of SDK completeness.", "evidenceIds": [ "raygun-docs-14", "raygun-docs-15", "raygun-docs-8", "raygun-comm-1" ] }, { "productId": "raygun", "storyId": "agentic-webhooks", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Raygun's evidence shows integrations with Slack and PagerDuty for alerting (raygun-docs-16, raygun-docs-17), but there is no documentation of a general-purpose webhook subscription mechanism that would let an AI-native user programmatically subscribe to events. No API/webhook docs, no OpenAPI spec (raygun-probe-2 shows 404s), and no llms.txt (raygun-probe-1) to indicate agent-friendly event subscription.", "evidenceIds": [ "raygun-docs-16", "raygun-docs-17", "raygun-probe-1", "raygun-probe-2" ] }, { "productId": "raygun", "storyId": "ai-fix-pull-request", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Raygun's AI Resolution feature only chats/suggests root-cause fixes within its dashboard (raygun-docs-4, raygun-docs-5, raygun-docs-27); there is no evidence it drafts code changes or opens a pull request against a connected repo despite repo integrations existing for source maps (raygun-docs-2).", "evidenceIds": [ "raygun-docs-4", "raygun-docs-5", "raygun-docs-27", "raygun-docs-2" ] }, { "productId": "raygun", "storyId": "ai-root-cause", "verdict": "partial", "quality": 7, "confidence": "medium", "rationale": "Raygun's 'AI Resolution' feature explicitly uses AI (OpenAI/Azure OpenAI) to suggest root causes and fixes for errors, working from stack trace/class name/error data, and integrates with repos (GitHub/Bitbucket/GitLab) for source context, matching the core of this story. However, evidence doesn't explicitly confirm the AI ingests breadcrumbs or correlates specific commits as part of its reasoning, and there's no independent/hands-on validation of the suggestion quality. Missing for 10: explicit evidence of breadcrumb/commit-level correlation feeding the AI analysis, and independent corroboration of real-world root-cause accuracy.", "evidenceIds": [ "raygun-docs-4", "raygun-docs-27", "raygun-docs-5", "raygun-docs-2", "raygun-docs-1" ] }, { "productId": "raygun", "storyId": "alert-routing-channels", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Raygun documents Slack and PagerDuty integrations tied to alert rules (raygun-docs-16, raygun-docs-17), and error grouping/prioritization by occurrence and affected users (raygun-docs-3, raygun-docs-7) supports frequency-spike and affected-user alerting concepts, but there's no evidence of unified rule configuration combining project, environment, and severity dimensions, no documented email routing, and no explicit per-rule threshold UI for new-issue vs frequency-spike vs affected-user-count conditions. missing for 10: documented per-rule threshold configuration (new issue, frequency spike, affected-user count) in one place, email alert routing, environment/severity-based routing logic, and independent confirmation of noise-reduction effectiveness.", "evidenceIds": [ "raygun-docs-16", "raygun-docs-17", "raygun-docs-3", "raygun-docs-7" ] }, { "productId": "raygun", "storyId": "api-interactive-docs", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of an interactive API reference or runnable examples; probes explicitly show no OpenAPI/swagger spec is exposed and no llms.txt, and none of the docs mention an interactive/try-it API console. Missing for 10: interactive API explorer UI, runnable code examples, OpenAPI/swagger spec.", "evidenceIds": [ "raygun-probe-1", "raygun-probe-2" ] }, { "productId": "raygun", "storyId": "api-machine-spec", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Direct probes for OpenAPI/swagger spec files all returned 404, and no documentation evidence references a downloadable machine-readable API spec; only an MCP server exists, which is a different capability.", "evidenceIds": [ "raygun-probe-2", "raygun-probe-1" ] }, { "productId": "raygun", "storyId": "api-sandbox", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Raygun is an error/crash/performance monitoring product, not a data platform or API service with a sandbox/test environment concept; testing against production vs sandbox data is a category error for this kind of tool.", "evidenceIds": [] }, { "productId": "raygun", "storyId": "api-versioning-policy", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence of API versioning scheme or a documented deprecation policy; probes for OpenAPI/spec files return 404s and no changelog entries discuss API versioning or deprecation.", "evidenceIds": [ "raygun-probe-2", "raygun-docs-8" ] }, { "productId": "raygun", "storyId": "automation-bulk-operations", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Raygun's Crash Reporting export feature lets users export all or selected error groups at once, and 'ignore by rule' can suppress errors matching a condition across many occurrences, indicating some set-level operations exist. However, there's no evidence of AI-driven or programmatic bulk actions (e.g., bulk resolve, bulk tag, bulk delete via API/MCP) that an AI-native user could invoke across many items. missing for 10: bulk resolve/update actions, API or MCP-driven bulk operation examples, independent confirmation of bulk workflows at scale.", "evidenceIds": [ "raygun-docs-9", "raygun-docs-11", "raygun-docs-26" ] }, { "productId": "raygun", "storyId": "automation-rules-engine", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Raygun supports rule-based triggers for alerting integrations — e.g. PagerDuty incidents created 'when an issue is triggered from this rule' and Slack channel notifications for APM traces — plus 'ignore by rule' conditions for error handling, showing some automated event-to-action capability. However, this is limited to notification/ignore rules rather than a general-purpose automation engine for arbitrary actions on arbitrary events. Missing for 10: evidence of a broader rule/condition engine (e.g. custom triggers, multi-step workflows, webhooks-as-actions beyond third-party integrations), and independent confirmation of reliability.", "evidenceIds": [ "raygun-docs-17", "raygun-docs-16", "raygun-docs-11", "raygun-docs-26" ] }, { "productId": "raygun", "storyId": "automation-scheduled-jobs", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Raygun is an error/crash/performance monitoring platform, not a workflow/job orchestration tool; scheduling recurring jobs or automation workflows is outside its product category and axis.", "evidenceIds": [] }, { "productId": "raygun", "storyId": "automation-versioned-workflows", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Raygun is an error/crash/performance monitoring product, not an automation-building tool with workflows or rules that would need versioning, review, or rollback. There is no concept of 'automations' in Raygun's product surface (error grouping, ignore rules, and AI resolution chat are not automation pipelines) — this axis is a category error for this product type.", "evidenceIds": [] }, { "productId": "raygun", "storyId": "breadcrumbs-custom-context", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Docs mention filtering/segmenting errors by 'custom tags' (raygun-docs-3) and removing sensitive data before sending (raygun-docs-12), implying some ability to attach custom metadata to events, but there is no evidence of a breadcrumb API, explicit user-context attachment, or SDK instrumentation guidance for attaching state to individual events. Missing for 10: breadcrumb trail API, explicit user identification/context object, SDK code examples showing tags/custom data attachment per event.", "evidenceIds": [ "raygun-docs-3", "raygun-docs-12" ] }, { "productId": "raygun", "storyId": "error-grouping-dedup", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Raygun documents stack-trace fingerprinting grouping by exception type, class name, method name, and stack trace location [raygun-docs-7], which covers the core deduplication claim. However, there is no evidence of a mechanism to customize or override grouping when it misfires (e.g., manual merge/split of error groups); only unrelated workflow controls like ignore rules and resolved statuses are documented [raygun-docs-11][raygun-docs-26][raygun-docs-10]. Missing for 10: documented ability to manually re-group, split, or merge error groups when fingerprinting is wrong, and any independent/hands-on confirmation of grouping accuracy or customization.", "evidenceIds": [ "raygun-docs-7", "raygun-docs-11", "raygun-docs-26", "raygun-docs-10" ] }, { "productId": "raygun", "storyId": "error-trend-dashboards", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Raygun provides error grouping, filtering/sorting by occurrence and users affected, an error chart on search results, and export capabilities, which support trend visibility within a single project, but there is no documented cross-project/cross-team rollup dashboard, no explicit 'new issues per release' or 'top regressions' aggregate view, and no evidence of an engineering-leader-focused multi-team overview. missing for 10: cross-project/team-level aggregate dashboards, explicit release-over-release regression tracking, top-regressions ranking view, and independent confirmation of these executive-level views.", "evidenceIds": [ "raygun-docs-3", "raygun-docs-19", "raygun-docs-29", "raygun-docs-9", "raygun-docs-21" ] }, { "productId": "raygun", "storyId": "event-search-queries", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Docs confirm smart filters to segment errors by date, location, version, host, OS, browser, and custom tags, plus a dedicated search results page with visual charts, indicating some query/filter capability. However, there's no evidence of a documented query syntax, explicit 'environment' as a filter dimension, or cross-project search scope. missing for 10: documented query syntax/operators, explicit environment tag filtering, cross-project search evidence.", "evidenceIds": [ "raygun-docs-3", "raygun-docs-19", "raygun-docs-18" ] }, { "productId": "raygun", "storyId": "issue-tracker-sync", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Evidence shows GitHub/Bitbucket/GitLab integration is for repo linking and source map decoding, not issue-tracker ticket sync; no mention of Jira, GitHub Issues, or Linear ticket integration with two-way status sync (auto-resolve/reopen) anywhere in the pack.", "evidenceIds": [ "raygun-docs-2", "raygun-docs-10" ] }, { "productId": "raygun", "storyId": "issue-triage-workflow", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Raygun documents resolve/ignore (with time/occurrence/rule conditions) as part of an explicit error-status triage workflow, but evidence never mentions assigning an owner, a snooze action, or automatic reopening of resolved issues upon regression. missing for 10: owner assignment, explicit snooze action, automatic reopen-on-regression behavior, independent confirmation of these workflows.", "evidenceIds": [ "raygun-docs-10", "raygun-docs-11", "raygun-docs-25", "raygun-docs-26" ] }, { "productId": "raygun", "storyId": "issues-api-read", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Docs mention an external access token used for uploads (source maps, dSYM, ProGuard) and a Deployments API, but there is no evidence of a documented REST API for listing, querying, or updating issues or fetching raw error events, and probes for OpenAPI/swagger specs and llms.txt all returned 404s.", "evidenceIds": [ "raygun-docs-8", "raygun-docs-23", "raygun-probe-1", "raygun-probe-2" ] }, { "productId": "raygun", "storyId": "js-sourcemap-unminify", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Docs confirm source map support: automatic fetching if public, or manual upload via the JS source map center or API, and an external access token is required for 'Uploading JavaScript files directly to Raygun' — implying scriptable/CI-friendly upload. However, no evidence of an official CLI tool or bundler plugin (webpack/vite/rollup) for source map upload as CI step. Missing for 10: dedicated CLI/bundler plugin documentation, explicit CI pipeline integration examples.", "evidenceIds": [ "raygun-docs-14", "raygun-docs-15", "raygun-docs-8" ] }, { "productId": "raygun", "storyId": "merge-split-issues", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Docs describe automatic grouping logic (exception type, class name, method, stack trace location) and error statuses (resolved/ignored) but there is no evidence of a manual merge-issues or split-issues feature to correct fingerprinting mistakes.", "evidenceIds": [ "raygun-docs-7", "raygun-docs-25", "raygun-docs-10", "raygun-docs-11" ] }, { "productId": "raygun", "storyId": "mobile-crash-reporting", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Docs show support for iOS (dSYM upload) and Android (ProGuard mapping file upload) native crash symbolication, implying native mobile crash reporting exists, and general crash docs mention environment/version context capture. However there's no explicit mention of NDK crash support, and no mobile-specific documentation of device/OS/app-version metadata capture for reproducing crashes. missing for 10: NDK-specific crash handling evidence, mobile SDK docs detailing device/OS/app-version context fields, independent/hands-on confirmation of mobile crash capture.", "evidenceIds": [ "raygun-docs-8", "raygun-docs-1" ] }, { "productId": "raygun", "storyId": "native-symbolication", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Docs confirm Raygun supports sending dSYMs and uploading ProGuard mapping files (gated behind an external access token), which implies symbolication support for iOS/Android crashes, but there is no detailed documentation of the upload workflow, verification of resulting symbolication quality, or independent/hands-on confirmation it works as claimed. missing for 10: detailed dSYM/ProGuard upload workflow docs, native debug symbol handling details, independent verification of correct symbolication.", "evidenceIds": [ "raygun-docs-8", "raygun-docs-23" ] }, { "productId": "raygun", "storyId": "noise-spike-controls", "verdict": "partial", "quality": 4, "confidence": "medium", "rationale": "Raygun offers ignore/mute rules (by time period, occurrence count, or custom rule) and PagerDuty/Slack alert integrations, which help reduce noise, but there's no evidence of spike protection or per-key rate limiting specifically designed to suppress alert floods during a bad deploy. missing for 10: spike/anomaly-based alert suppression, per-key or per-service rate limiting on alert volume, deploy-aware noise suppression, and any hands-on/independent confirmation these mute rules actually prevent night-long paging.", "evidenceIds": [ "raygun-docs-11", "raygun-docs-26", "raygun-docs-25", "raygun-docs-17", "raygun-docs-16" ] }, { "productId": "raygun", "storyId": "open-ingest-protocol", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence of a documented open ingestion protocol/API spec for sending events; probes show no OpenAPI/llms.txt spec published, and docs only reference official SDKs and an external access token for specific upload services, not a general custom-client ingestion protocol. Missing for 10: public API/protocol reference for event ingestion, evidence of third-party or community-built compatible clients.", "evidenceIds": [ "raygun-probe-1", "raygun-probe-2", "raygun-docs-8" ] }, { "productId": "raygun", "storyId": "openness-api-parity", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Docs reference specific API endpoints (Deployments API, external access token for uploads, source map API) showing some functionality is exposed via API, but there is no evidence of comprehensive API parity with the UI, and probes show no discoverable OpenAPI spec or llms.txt (404s), suggesting the API surface is not fully documented or complete. missing for 10: evidence of full CRUD/API coverage for error management, AI resolution chat, exports, and dashboards equivalent to UI; a published OpenAPI/swagger spec confirming parity.", "evidenceIds": [ "raygun-docs-8", "raygun-docs-15", "raygun-docs-9", "raygun-probe-1", "raygun-probe-2" ] }, { "productId": "raygun", "storyId": "openness-full-export", "verdict": "partial", "quality": 3, "confidence": "medium", "rationale": "Raygun documents a Crash Reporting export feature (export all or selected error groups) and CSV exports aligned with filters, showing some data portability, but there is no evidence of a full account-wide export covering APM, RUM, and other modules in open formats, and no documented API/OpenAPI spec (probes return 404) that would enable complete self-service data extraction. missing for 10: full-account data export beyond error groups, documented open API/schema for export, evidence of exporting APM/RUM/user data, independent confirmation of format openness.", "evidenceIds": [ "raygun-docs-9", "raygun-docs-18", "raygun-docs-28", "raygun-probe-2" ] }, { "productId": "raygun", "storyId": "openness-open-license", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Raygun is a closed, commercial SaaS product with no evidence of an open-license source code repository for the core product; evidence only shows a separate MCP server integration and docs, not open-sourced product source.", "evidenceIds": [] }, { "productId": "raygun", "storyId": "openness-self-host", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Raygun is offered as a hosted SaaS monitoring platform; no evidence in the pack mentions a self-hosted deployment option, on-prem package, or Docker image. Absence of evidence for an applicable capability yields none. missing for 10: any self-hosting/on-prem documentation, deployment instructions, or licensing for self-managed instances.", "evidenceIds": [] }, { "productId": "raygun", "storyId": "privacy-data-residency", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack mentions data residency, region selection, or where Raygun stores customer data; only sensitive-data filtering and IP-storage disabling are documented, which are data-scrubbing controls, not region/residency choice.", "evidenceIds": [] }, { "productId": "raygun", "storyId": "privacy-no-training", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "Raygun uses OpenAI/Azure OpenAI for its AI-powered error resolution feature (raygun-docs-27), but there is no evidence of any opt-out mechanism, data usage policy, or AI training exclusion controls for customer data. Evidence covers sensitive data filtering for error reports (docs-12, docs-13) but not AI training opt-out specifically.", "evidenceIds": [ "raygun-docs-27", "raygun-docs-12", "raygun-docs-13" ] }, { "productId": "raygun", "storyId": "privacy-retention-controls", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Raygun provides some data controls relevant to retention/deletion — filtering sensitive data before it's sent, disabling IP address storage, and exporting error data — but there's no documented self-service deletion/purge mechanism, configurable retention periods, or explicit data deletion policy for AI-native/automated control. missing for 10: documented retention period settings, an explicit data deletion/purge API or workflow, and confirmation these controls are accessible/automatable for AI-native use cases.", "evidenceIds": [ "raygun-docs-12", "raygun-docs-13", "raygun-docs-9" ] }, { "productId": "raygun", "storyId": "privacy-telemetry-optout", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Raygun documents limited privacy controls—filtering sensitive data before it's sent to Raygun and disabling IP address storage—but these apply to the error/crash data customers send from their apps, not to opting out of Raygun's own product usage/telemetry tracking for an AI-native user of the platform. Missing for 10: explicit opt-out mechanism for Raygun's own usage analytics/telemetry, any AI-agent-specific telemetry controls, and independent confirmation these settings fully disable tracking.", "evidenceIds": [ "raygun-docs-12", "raygun-docs-13", "raygun-docs-24" ] }, { "productId": "raygun", "storyId": "quota-spend-controls", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence anywhere in the pack of per-project event quotas, spend limits, or budget caps for error volumes; docs focus on error grouping, ignoring rules, AI resolution, and integrations, not billing/quota controls.", "evidenceIds": [] }, { "productId": "raygun", "storyId": "regression-alerts", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Raygun's docs show error grouping, marking errors 'Resolved', and version-based filtering, but there is no evidence of a distinct 'regression' state or alert that fires specifically when a previously resolved error reappears in a newer release version, separate from ordinary new-error notifications.", "evidenceIds": [ "raygun-docs-7", "raygun-docs-10", "raygun-docs-25", "raygun-docs-3" ] }, { "productId": "raygun", "storyId": "release-health-crash-free", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows Raygun can filter/segment errors by version and other tags (raygun-docs-3) but there is no mention of a release-health dashboard, crash-free sessions/users metrics, adoption-per-release tracking, or a canary-vs-stable comparison view anywhere in the docs or changelog.", "evidenceIds": [ "raygun-docs-3" ] }, { "productId": "raygun", "storyId": "release-tracking-commits", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Raygun docs show native repo integrations (GitHub/Bitbucket/GitLab) with source map decoding and a Deployments API requiring an external access token, implying some deployment/version tracking, but there is no explicit documentation of a 'first seen in release' field or automated 'suspect commit' identification tied to an issue. missing for 10: explicit release-tagging of first occurrence, suspect-commit/blame attribution, and any hands-on confirmation this works as described.", "evidenceIds": [ "raygun-docs-2", "raygun-docs-8", "raygun-docs-3" ] }, { "productId": "raygun", "storyId": "scm-blame-context", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Raygun documents native repo integrations (GitHub, Bitbucket, GitLab) with source map decoding, which implies some code-context mapping from stack traces, but there is no evidence of surfacing suspect commits, code owners, or direct deep-linking from a stack frame to the exact line on the default branch. missing for 10: suspect-commit identification, code owner attribution, verified line-level deep-linking to source repo.", "evidenceIds": [ "raygun-docs-2", "raygun-docs-14", "raygun-docs-15" ] }, { "productId": "raygun", "storyId": "sdk-broad-coverage", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Raygun's crash-reporting docs confirm automatic capture of stack traces, environment, browser/host info (raygun-docs-1) and mention platform-specific tooling like JS source maps (raygun-docs-14/15), iOS dSYM uploads and Android ProGuard mapping (raygun-docs-8/23), plus a community testimonial confirming .NET and WinRT SDK support (raygun-comm-1). However, there is no explicit documentation snippet showing SDK installation/auto-instrumentation code for each platform (frontend, backend, mobile) or confirmation that uncaught exceptions are captured without manual instrumentation across all three. Missing for 10: explicit SDK setup docs per platform, confirmation of automatic uncaught-exception hooking (not just crash reporting features), and independent verification beyond one old HN comment.", "evidenceIds": [ "raygun-docs-1", "raygun-docs-8", "raygun-docs-14", "raygun-docs-15", "raygun-docs-23", "raygun-comm-1" ] }, { "productId": "raygun", "storyId": "server-side-data-scrubbing", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Raygun docs confirm SDK-level filtering of sensitive data before it's sent (client-side scrubbing) and an IP-address suppression toggle, which addresses part of the story. However, there is no evidence of server-side scrubbing rules applied after ingestion, no mention of PII-specific pattern rules (e.g., regex-based field scrubbing), and no confirmation that filtered data never persists server-side. missing for 10: server-side scrubbing rule engine, documented PII-pattern/regex filtering, confirmation of non-persistence guarantees, independent verification of scrubbing efficacy.", "evidenceIds": [ "raygun-docs-12", "raygun-docs-13", "raygun-docs-24" ] }, { "productId": "raygun", "storyId": "user-feedback-on-errors", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack covers Raygun's crash reporting stack traces, AI resolution, error grouping, source maps, and integrations, but contains no mention of a crash-report dialog or feedback widget that lets end users submit feedback tied to a specific error event — a capability common in comparable crash-reporting tools.", "evidenceIds": [] }, { "productId": "rollbar", "storyId": "affected-users-quantified", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Rollbar explicitly tracks which People (users) are affected by each error (rollbar-docs-20/39), giving a per-item user-impact metric, and pricing/session-replay references confirm session-level tracking exists (rollbar-docs-21/40, rollbar-gh-3). However, there is no direct evidence of a unified 'affected users + affected sessions' metric surfaced on each Item for blast-radius-based prioritization, nor of sorting/filtering by this metric. Missing for 10: explicit item-level 'affected users/sessions count' UI documentation, evidence of sorting/prioritizing items by this metric rather than raw occurrence count.", "evidenceIds": [ "rollbar-docs-20", "rollbar-docs-39", "rollbar-docs-21", "rollbar-docs-40", "rollbar-gh-3" ] }, { "productId": "rollbar", "storyId": "agent-triages-errors", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Rollbar has a documented REST API with filters for pulling top production, error/critical items (rollbar-docs-15) and stack trace data (rollbar-docs-5), plus an official MCP server for AI systems (rollbar-docs-18, rollbar-probe-3) and an AI agent 'Resolve' that can act on production errors (rollbar-docs-17). Tracker integrations like JIRA exist (implied by rollbar-comm-4's mention of JIRA credentials) but the evidence pack has no documented API/MCP workflow for an agent to actually triage and file a ticket into an external tracker. Missing for 10: explicit docs on programmatic bug-filing/ticket-creation via API or MCP, and independent confirmation of an end-to-end triage-to-ticket workflow.", "evidenceIds": [ "rollbar-docs-15", "rollbar-docs-5", "rollbar-docs-14", "rollbar-docs-18", "rollbar-probe-3", "rollbar-docs-17", "rollbar-comm-4" ] }, { "productId": "rollbar", "storyId": "agentic-agent-docs", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Rollbar hosts a live llms.txt at docs.rollbar.com/llms.txt (HTTP 200, confirmed via probe) and also provides agent-oriented docs for its MCP server setup, directly enabling an agent to be pointed at machine-readable documentation. missing for 10: no independent/community corroboration of llms.txt usage in the wild, and no evidence of additional agent-friendly formats (e.g., structured API schemas, since openapi.json returned 404s).", "evidenceIds": [ "rollbar-probe-1", "rollbar-docs-18", "rollbar-probe-3", "rollbar-probe-2" ] }, { "productId": "rollbar", "storyId": "agentic-ai-insights", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Rollbar's 'Resolve' AI agent performs root-cause analysis on production errors and produces a pull request, and Rollbar also ships an MCP server for connecting AI systems to its data — both indicate AI-generated insights/suggestions surfaced in-product. However, evidence is limited to brief doc snippets with no detail on the scope of insights, UI presentation, or independent/hands-on validation. Missing for 10: detailed documentation of what 'insights' look like beyond PR generation, independent/hands-on confirmation of quality, and broader in-product AI analytics beyond root-cause/PR suggestions.", "evidenceIds": [ "rollbar-docs-17", "rollbar-docs-37", "rollbar-docs-18", "rollbar-probe-3" ] }, { "productId": "rollbar", "storyId": "agentic-autonomous-automation", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Rollbar documents 'Resolve,' an AI agent that autonomously analyzes production errors and produces a pull request without manual triggering, plus webhooks/MCP server that could support background automation pipelines. However, documentation is thin on configuration of ongoing/scheduled automations, and there is no independent/hands-on evidence of it running unattended over time. Missing for 10: detailed setup/config docs for autonomous scheduling, independent verification of unattended operation, and broader automation examples beyond the single Resolve feature.", "evidenceIds": [ "rollbar-docs-17", "rollbar-docs-37", "rollbar-docs-18", "rollbar-probe-3", "rollbar-docs-11" ] }, { "productId": "rollbar", "storyId": "agentic-builtin-assistant", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Rollbar documents 'Resolve,' a built-in AI agent that takes a production error and produces a reviewable pull request, which is a form of task delegation to an in-product AI assistant. However, evidence is limited to a couple of short doc snippets with no workflow detail, UI walkthrough, or independent/hands-on corroboration of its effectiveness. Missing for 10: detailed documentation of how tasks are delegated/configured, broader assistant capabilities beyond RCA/PR generation, and independent user reports validating real-world use.", "evidenceIds": [ "rollbar-docs-17", "rollbar-docs-37" ] }, { "productId": "rollbar", "storyId": "agentic-headless", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Rollbar's REST API and deploy-tracking notifications are designed to be called programmatically (e.g., from CI pipelines) to report deploys and query items headlessly, and access tokens support such automation. However there is no explicit CLI, docs page for CI integration, or GitHub Actions example demonstrating headless CI usage. Missing for 10: dedicated CI/CD documentation or CLI tool, explicit examples of running Rollbar operations headlessly in pipelines, and independent confirmation of such usage.", "evidenceIds": [ "rollbar-docs-7", "rollbar-docs-14", "rollbar-docs-16", "rollbar-docs-28", "rollbar-docs-34", "rollbar-docs-36" ] }, { "productId": "rollbar", "storyId": "agentic-mcp-client", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence only shows Rollbar exposing its own MCP server (docs-18, probe-3) so external AI agents can call Rollbar's tools — the opposite direction of this story, which asks whether Rollbar itself can consume/plug in external MCP servers to use their tools. No evidence exists of Rollbar acting as an MCP client or of its 'Resolve' AI agent consuming external MCP tools.", "evidenceIds": [ "rollbar-docs-18", "rollbar-probe-3", "rollbar-docs-17" ] }, { "productId": "rollbar", "storyId": "agentic-mcp-server", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Rollbar is not itself an AI agent but an error-monitoring SaaS, so the axis applies; docs explicitly describe an official MCP server with a dedicated setup page (rollbar-docs-18, rollbar-probe-3) confirmed live via probe. missing for 10: independent/hands-on corroboration of the MCP server working, and details on which tools/data it exposes.", "evidenceIds": [ "rollbar-docs-18", "rollbar-probe-3" ] }, { "productId": "rollbar", "storyId": "agentic-nl-commands", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Rollbar ships an official MCP server (rollbar-docs-18, rollbar-probe-3) that lets AI systems/tools connect to Rollbar data, and an AI agent 'Resolve' that turns errors into PRs (rollbar-docs-17), implying some natural-language/agentic operation is possible. However there is no documentation of actual natural-language command syntax, examples of AI-native interactions, or independent hands-on confirmation that this works well. missing for 10: example natural-language commands/queries via the MCP server, hands-on/independent verification of the MCP server or Resolve agent working as described, broader coverage beyond these two features.", "evidenceIds": [ "rollbar-docs-18", "rollbar-probe-3", "rollbar-docs-17" ] }, { "productId": "rollbar", "storyId": "agentic-official-cli", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence pack item mentions an official Rollbar CLI; documentation covers SDKs, REST API, and an MCP server but nothing about a command-line tool for AI-native workflows.", "evidenceIds": [] }, { "productId": "rollbar", "storyId": "agentic-public-api", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Rollbar publishes a RESTful public API with reference documentation, example endpoints (list items, filter by severity/environment), and access-token management for authentication, plus additional agentic surfaces like an MCP server and 'Resolve' AI agent. Missing for 10: a discoverable machine-readable OpenAPI/swagger spec (probe found only 404s) and independent/community confirmation of API usage.", "evidenceIds": [ "rollbar-docs-14", "rollbar-docs-15", "rollbar-docs-34", "rollbar-docs-35", "rollbar-docs-16", "rollbar-docs-36", "rollbar-docs-18", "rollbar-probe-3", "rollbar-probe-2" ] }, { "productId": "rollbar", "storyId": "agentic-scoped-keys", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Rollbar's access-token docs show project/account tokens can be restricted to narrow scopes (e.g. post_client_item-only tokens) and are now encrypted, which supports issuing least-privilege credentials that an MCP-connected agent could use, but there's no dedicated 'agent credential' workflow and a community report notes that default project tokens have standing read access that is not opt-in and never expires, undercutting a clean least-privilege story. missing for 10: agent-specific credential provisioning docs, confirmation that default tokens are scoped-by-default rather than broad, independent verification that scoped tokens work end-to-end with the MCP server/Resolve agent.", "evidenceIds": [ "rollbar-docs-41", "rollbar-docs-16", "rollbar-docs-36", "rollbar-docs-18", "rollbar-comm-1" ] }, { "productId": "rollbar", "storyId": "agentic-sdks", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Rollbar documents official SDKs across a dozen+ platforms (server and client-side, e.g. rollbar.js supporting React, Angular, Express, Next.js) backed by a REST API used by these SDKs, which supports building AI-native integrations against them. However, evidence lacks explicit AI-native framework integrations, language coverage details, or SDK quality/maintenance signals beyond basic docs. missing for 10: independent corroboration of SDK reliability/maintenance, broader language/platform SDK list, AI-specific SDK usage examples beyond MCP server.", "evidenceIds": [ "rollbar-docs-1", "rollbar-gh-4", "rollbar-docs-34", "rollbar-docs-14" ] }, { "productId": "rollbar", "storyId": "agentic-webhooks", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Rollbar has documented webhook support letting it push event data to arbitrary external services, which fits the 'subscribe via webhooks' story, and there's an example of using Hookdeck to transform webhook payloads for Discord. Missing for 10: detailed webhook payload schema/event-type documentation, signing/verification details, and independent/hands-on confirmation of reliability.", "evidenceIds": [ "rollbar-docs-11", "rollbar-docs-42" ] }, { "productId": "rollbar", "storyId": "ai-fix-pull-request", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Rollbar's docs describe 'Resolve', an AI agent that takes a production error and produces a pull request for human review, directly matching the story. However, evidence is limited to first-party docs with no independent/hands-on corroboration or detail on fix quality. missing for 10: independent/hands-on validation of Resolve's PR quality, details on review workflow, and evidence of real-world usage outcomes.", "evidenceIds": [ "rollbar-docs-17", "rollbar-docs-37" ] }, { "productId": "rollbar", "storyId": "ai-root-cause", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Rollbar's 'Resolve' AI agent explicitly takes a production error and produces a root-cause-oriented PR, matching the story's intent of analyzing stack traces/breadcrumbs/commits and proposing an actionable fix; this is corroborated by breadcrumb telemetry and stack trace/version tracking features that feed such analysis. However, the docs description is thin (a single sentence, no detail on how commits/breadcrumbs are actually used in the analysis) and there's no independent/hands-on validation of its accuracy or effectiveness. Missing for 10: detailed documentation of the reasoning process, evidence of breadcrumb/commit correlation specifically, and third-party corroboration of the root-cause quality.", "evidenceIds": [ "rollbar-docs-17", "rollbar-docs-37", "rollbar-gh-1", "rollbar-docs-6" ] }, { "productId": "rollbar", "storyId": "alert-routing-channels", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Rollbar's docs confirm real-time notifications routed to Slack, PagerDuty, email, and arbitrary webhooks (rollbar-docs-8,9,10,11,29,30,31), and item/API filtering by environment and severity (e.g., 'error or critical, in the production environment' - rollbar-docs-15,35) plus per-user tracking (rollbar-docs-20,39) that could feed affected-user alerts. However, no evidence explicitly documents configurable per-rule thresholds for frequency spikes or affected-user-count triggers as distinct alerting conditions. Missing for 10: explicit documentation of threshold-based alert rules (frequency spike, affected-user count triggers), and independent/hands-on confirmation that these rule types work as configured.", "evidenceIds": [ "rollbar-docs-8", "rollbar-docs-9", "rollbar-docs-10", "rollbar-docs-11", "rollbar-docs-15", "rollbar-docs-20", "rollbar-docs-29", "rollbar-docs-31" ] }, { "productId": "rollbar", "storyId": "api-interactive-docs", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Rollbar publishes a REST API reference with example requests/responses (e.g. list-all-items examples), suggesting a structured API reference exists, but there is no evidence of interactive 'try it' runnable examples, and probes for an OpenAPI spec (which typically powers interactive references) all returned 404s. Missing for 10: confirmation of an interactive try-it console, discoverable OpenAPI/Swagger spec, and any hands-on evidence of running examples directly from the docs.", "evidenceIds": [ "rollbar-docs-14", "rollbar-docs-15", "rollbar-docs-34", "rollbar-docs-35", "rollbar-probe-2" ] }, { "productId": "rollbar", "storyId": "api-machine-spec", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Rollbar publishes a REST API reference (rollbar-docs-14/34) but a direct probe for a machine-readable spec found no accessible OpenAPI/Swagger file at any standard location (rollbar-probe-2 shows 404s across openapi.json, swagger.json, etc.), and no evidence names a downloadable spec elsewhere.", "evidenceIds": [ "rollbar-docs-14", "rollbar-docs-34", "rollbar-probe-2" ] }, { "productId": "rollbar", "storyId": "api-sandbox", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence of a sandbox/test environment feature separate from production; Rollbar's docs discuss production error tracking, deploy tracking, and data retention but never mention a sandbox mode for testing without touching production data.", "evidenceIds": [] }, { "productId": "rollbar", "storyId": "api-versioning-policy", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "The evidence shows Rollbar has a REST API and reference docs, but there is no mention of API versioning scheme or a documented deprecation policy anywhere in the pack; a probe for OpenAPI spec even 404s. missing for 10: versioning scheme details, deprecation policy documentation, changelog for API version sunset dates.", "evidenceIds": [ "rollbar-docs-14", "rollbar-docs-34", "rollbar-probe-2" ] }, { "productId": "rollbar", "storyId": "automation-bulk-operations", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Rollbar's REST API supports listing items in bulk (e.g. paginated queries by status/environment) and item merging combines multiple items at once, and webhooks/API tokens allow programmatic bulk actions, but there is no documented bulk-update/bulk-resolve/bulk-delete endpoint or UI feature for acting on many items simultaneously. missing for 10: dedicated bulk-update/bulk-resolve/bulk-delete API or UI, batch operation examples, evidence of AI-native agents invoking bulk actions via MCP server, and any independent confirmation of bulk operation scale.", "evidenceIds": [ "rollbar-docs-14", "rollbar-docs-15", "rollbar-docs-4", "rollbar-docs-11", "rollbar-probe-3" ] }, { "productId": "rollbar", "storyId": "automation-rules-engine", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Rollbar supports webhooks that push data to external services on events, and notification integrations (Slack, PagerDuty, email) that fire alerts based on error events, which is a basic form of automated action-on-event. However there is no evidence of a full customizable rules engine (conditional logic, multi-step actions, or AI-native trigger definitions) beyond notification/webhook configuration. Missing for 10: documented conditional rule builder, evidence of complex multi-action automation, and any AI-native rule authoring beyond webhooks/notifications.", "evidenceIds": [ "rollbar-docs-11", "rollbar-docs-8", "rollbar-docs-9", "rollbar-docs-10", "rollbar-docs-29" ] }, { "productId": "rollbar", "storyId": "automation-scheduled-jobs", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Rollbar is an error-monitoring/observability tool, not a workflow/job scheduler; scheduling recurring jobs or workflows is outside its product category and there is no evidence of such a feature.", "evidenceIds": [] }, { "productId": "rollbar", "storyId": "automation-versioned-workflows", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Rollbar is an error-monitoring/observability platform, not a workflow/automation builder; it has no concept of user-created 'automations' that would need versioning, review, or rollback (its config rules like fingerprinting or webhooks are static settings, not versioned automation pipelines). This axis is a category mismatch for the product type.", "evidenceIds": [] }, { "productId": "rollbar", "storyId": "breadcrumbs-custom-context", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Rollbar's SDK docs and GitHub README confirm breadcrumbs via the 'telemetry timeline' feature and automatic person/user tracking to attach user context to errors, supporting the core of the story. However, there is no explicit documentation of custom tag/context attachment beyond built-in fields, so the full 'custom tags' capability is unevidenced. Missing for 10: explicit custom tag/metadata API documentation, independent/hands-on confirmation of combined breadcrumb+tag+user context on a single event.", "evidenceIds": [ "rollbar-gh-1", "rollbar-gh-5", "rollbar-docs-20", "rollbar-docs-39", "rollbar-docs-47" ] }, { "productId": "rollbar", "storyId": "error-grouping-dedup", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Rollbar's docs directly confirm the story: occurrences are aggregated into Items via default grouping/fingerprinting algorithm, custom fingerprinting rules let users override incorrect grouping, and item merging allows combining wrongly-separated items. This directly matches the developer story of automatic grouping plus manual correction. Missing for 10: independent/hands-on validation of grouping accuracy at scale and no detail on the exact stack-trace fingerprinting algorithm internals.", "evidenceIds": [ "rollbar-docs-2", "rollbar-docs-3", "rollbar-docs-4", "rollbar-docs-23" ] }, { "productId": "rollbar", "storyId": "error-trend-dashboards", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Rollbar exposes per-version and deploy tracking data and an items API that can be filtered by environment/severity, which give some raw material for volume-over-time and release-level views, but there is no evidence of a cross-project/team dashboard, 'top regressions' view, or leadership-level reporting UI. missing for 10: cross-project/team rollup dashboards, explicit 'top regressions' or trend visualizations, per-release new-issue counts UI, engineering-leader oriented reporting.", "evidenceIds": [ "rollbar-docs-6", "rollbar-docs-7", "rollbar-docs-15", "rollbar-docs-23" ] }, { "productId": "rollbar", "storyId": "event-search-queries", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Rollbar docs confirm advanced search/filtering by properties (rollbar-gh-2), API item listing filtered by status/environment/level (rollbar-docs-15/35), and tag/version/environment tracking (rollbar-docs-6, rollbar-docs-20), but there is no evidence of a documented real query syntax (operators, boolean logic, cross-project search) comparable to a query language, nor of searching across multiple projects simultaneously. missing for 10: documented query syntax/operators, cross-project search capability, evidence of filtering by arbitrary custom properties beyond basic API params.", "evidenceIds": [ "rollbar-gh-2", "rollbar-docs-15", "rollbar-docs-35", "rollbar-docs-6", "rollbar-docs-20", "rollbar-docs-14" ] }, { "productId": "rollbar", "storyId": "issue-tracker-sync", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence only confirms Rollbar has some integration with JIRA, GitHub, and Slack (mentioned in passing in a security breach discussion about credential storage), but there is no documentation of two-way status sync where resolving a linked ticket resolves the Rollbar error and a regression reopens it, and Linear is never mentioned at all.", "evidenceIds": [ "rollbar-comm-4", "rollbar-docs-12", "rollbar-docs-32" ] }, { "productId": "rollbar", "storyId": "issue-triage-workflow", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Rollbar docs confirm item Owner assignment (rollbar-docs-13/33) and four statuses—Active, Resolved, Muted, Archived (rollbar-docs-12/32)—covering assign/resolve/ignore-like (Muted) workflows, but there is no explicit mention of a 'snooze' function or documentation that a resolved item automatically reopens when the error regresses. missing for 10: explicit snooze capability, documented auto-reopen-on-regression behavior, independent/hands-on confirmation of triage workflow.", "evidenceIds": [ "rollbar-docs-12", "rollbar-docs-13", "rollbar-docs-32", "rollbar-docs-33" ] }, { "productId": "rollbar", "storyId": "issues-api-read", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Rollbar documents a RESTful API (rollbar-docs-14/34) with endpoints like list-all-items supporting filtering by status/environment (rollbar-docs-15/35) and scoped access/project tokens (rollbar-docs-16/36/41), which covers listing and querying issues with scoped tokens. However, the evidence pack has no explicit documentation of an 'update item' endpoint or a raw-event-fetch endpoint, and probes found no discoverable OpenAPI spec (rollbar-probe-2), and a community report flags a default non-expiring read token that isn't opt-in (rollbar-comm-1), raising some doubt about token scoping hygiene. Missing for 10: explicit update/PATCH issue endpoint documentation, explicit raw-event retrieval endpoint documentation, independent corroboration of full CRUD API coverage.", "evidenceIds": [ "rollbar-docs-14", "rollbar-docs-34", "rollbar-docs-15", "rollbar-docs-35", "rollbar-docs-16", "rollbar-docs-36", "rollbar-docs-41", "rollbar-probe-2", "rollbar-comm-1" ] }, { "productId": "rollbar", "storyId": "js-sourcemap-unminify", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Rollbar's docs confirm source map support: stack traces show original filename/line/method once source maps are provided, and JS minification requires supplying source maps (rollbar-docs-5, rollbar-docs-27). However, the evidence pack doesn't detail the specific CLI tool or bundler (webpack/vite) plugin, nor explicit CI upload workflow steps. Missing for 10: explicit CLI/bundler plugin documentation, CI pipeline integration examples, and independent confirmation that uploaded maps correctly de-minify traces in production.", "evidenceIds": [ "rollbar-docs-5", "rollbar-docs-27" ] }, { "productId": "rollbar", "storyId": "merge-split-issues", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Rollbar docs directly document merging items into one group (merge-items) and custom fingerprinting rules to split occurrences the default grouping wrongly combined/separated, covering both directions of the story. missing for 10: no independent/hands-on corroboration of merge/split workflow, and no detail on undo/split-after-merge UI specifics.", "evidenceIds": [ "rollbar-docs-3", "rollbar-docs-4", "rollbar-docs-25", "rollbar-docs-26" ] }, { "productId": "rollbar", "storyId": "mobile-crash-reporting", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Rollbar claims broad platform coverage ('over a dozen platforms') and has an Apple Unified Logging integration module, hinting at some mobile/OS-level telemetry support, but the evidence pack contains no explicit documentation of iOS, Android, or NDK crash-reporting SDKs, nor confirmation that device, OS, and app-version context is captured for native mobile crashes. Missing for 10: dedicated iOS SDK docs, Android/NDK crash SDK docs, explicit device/OS/app-version metadata capture for native mobile crashes, and any hands-on/community confirmation of mobile crash reproduction.", "evidenceIds": [ "rollbar-docs-1", "rollbar-docs-46" ] }, { "productId": "rollbar", "storyId": "native-symbolication", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Rollbar's docs mention source maps for minified JavaScript (rollbar-docs-5/27) and broad platform support (rollbar-docs-1), but there is no evidence in this pack of dSYM, ProGuard, or native debug-symbol upload/symbolication features for iOS/Android crash reports.", "evidenceIds": [ "rollbar-docs-1", "rollbar-docs-5", "rollbar-docs-27" ] }, { "productId": "rollbar", "storyId": "noise-spike-controls", "verdict": "partial", "quality": 4, "confidence": "medium", "rationale": "Rollbar clearly supports mute/ignore via item Active/Resolved/Muted/Archived statuses and configurable notification rules (Slack, PagerDuty, webhooks), which helps reduce noise, but there is no evidence of explicit 'spike protection' or 'per-key rate limiting' features to throttle alert volume during a bad deploy. missing for 10: spike protection mechanism, per-key/per-project rate limiting, documented anti-alert-storm configuration, independent SRE validation of noise reduction in practice.", "evidenceIds": [ "rollbar-docs-12", "rollbar-docs-32", "rollbar-docs-8", "rollbar-docs-29", "rollbar-docs-9", "rollbar-docs-10", "rollbar-docs-11" ] }, { "productId": "rollbar", "storyId": "open-ingest-protocol", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Docs confirm a RESTful API used by Rollbar's own SDKs to report errors and mention scoped client-side tokens for sending items (post_client_item), implying an underlying ingestion protocol exists, but there is no explicit publicly documented event schema, payload spec, or guide for building a custom/unofficial client, and probes found no OpenAPI/swagger spec for the ingestion endpoint. missing for 10: explicit item/event ingestion API reference with request schema, official statement that the protocol is open for third-party/custom SDKs, and independent evidence of community-built custom clients using it directly.", "evidenceIds": [ "rollbar-docs-34", "rollbar-docs-41", "rollbar-probe-2", "rollbar-docs-14" ] }, { "productId": "rollbar", "storyId": "openness-api-parity", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Rollbar has a documented RESTful API covering items, listing, resolving, access tokens, and more, and even an MCP server for AI-agent access, suggesting broad API parity with the UI. However, no evidence confirms full parity (e.g., no OpenAPI spec found, and some UI-only workflows like Slack/PagerDuty connection setup, notification configuration, and account/billing settings are not shown to have API equivalents). missing for 10: OpenAPI/full endpoint coverage confirmation, evidence that all UI actions (integrations setup, notification rules, billing/plan management) are API-exposed, independent confirmation of API-UI parity.", "evidenceIds": [ "rollbar-docs-14", "rollbar-docs-15", "rollbar-docs-34", "rollbar-docs-35", "rollbar-docs-18", "rollbar-probe-2", "rollbar-probe-3" ] }, { "productId": "rollbar", "storyId": "openness-full-export", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Rollbar exposes a RESTful API (rollbar-docs-14/34) that can list items and occurrences (rollbar-docs-15/35) in JSON, giving some programmatic access to data, but there is no documented bulk 'export all data' feature, no data-portability/GDPR export tool, and no mention of open-format full account export or an easy 'leave with your data' workflow. Missing for 10: an explicit data-export/download-all feature, documentation of export formats, and any account-closure data portability guarantee.", "evidenceIds": [ "rollbar-docs-14", "rollbar-docs-15", "rollbar-docs-34", "rollbar-docs-19" ] }, { "productId": "rollbar", "storyId": "openness-open-license", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows only client-side SDKs (e.g., rollbar.js) hosted on GitHub, but no indication that Rollbar's core error-monitoring platform/service is released under an open-source license or that its full source is publicly readable.", "evidenceIds": [] }, { "productId": "rollbar", "storyId": "openness-self-host", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Rollbar is a cloud SaaS product; evidence pack shows only pricing plans, hosted API, and cloud features with no mention of a self-hosted/on-prem deployment option or Docker/Kubernetes distribution.", "evidenceIds": [] }, { "productId": "rollbar", "storyId": "privacy-data-residency", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack mentions data region selection, residency options, or geographic hosting choices for Rollbar; only data retention period customization is documented, which is a distinct axis. Missing for 10: any mention of regional data centers, EU/US hosting options, or residency controls.", "evidenceIds": [] }, { "productId": "rollbar", "storyId": "privacy-no-training", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack addresses AI training data usage opt-out or any privacy policy statement about excluding customer data from model training; Rollbar's evidence covers error monitoring, integrations, and an AI 'Resolve' feature but nothing on this specific privacy control.", "evidenceIds": [] }, { "productId": "rollbar", "storyId": "privacy-retention-controls", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Rollbar docs confirm that data retention periods can be customized on paid plans, giving some retention control, but there is no evidence of explicit data-deletion controls (e.g., APIs or UI to purge specific records) or granular deletion policies suited to AI-native compliance needs. missing for 10: explicit deletion/purge controls, evidence of GDPR/CCPA-style data erasure workflow, independent confirmation of retention enforcement.", "evidenceIds": [ "rollbar-docs-19", "rollbar-docs-38" ] }, { "productId": "rollbar", "storyId": "privacy-telemetry-optout", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "Rollbar is itself an error-tracking/telemetry product, and its data-retention docs describe customization but no evidence describes an opt-out mechanism for Rollbar's own product usage/telemetry tracking (e.g., no privacy settings for disabling SDK telemetry collection or vendor analytics on the product itself). No documentation or community evidence addresses an opt-out control for AI-native users. Missing for 10: any documented telemetry/usage-tracking opt-out setting, privacy policy reference, or admin control disabling data collection.", "evidenceIds": [] }, { "productId": "rollbar", "storyId": "quota-spend-controls", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence only shows fixed plan-tier event allotments (e.g., '5K occurrences + 1K sessions') on the pricing page, not any user-configurable quota caps, hard spend limits, or overage-prevention controls per project. No docs describe throttling ingestion or capping billing when an error storm occurs.", "evidenceIds": [ "rollbar-docs-21", "rollbar-docs-40", "rollbar-docs-22" ] }, { "productId": "rollbar", "storyId": "regression-alerts", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "While Rollbar tracks item status (Active/Resolved/Muted/Archived) and can show which code versions produce errors, no evidence describes a distinct 'reopened/regression' alert that differentiates a previously-resolved error reappearing in a new release from an ordinary new-issue notification. Missing for 10: explicit documentation of reactivation/regression alerting tied to version/release tracking, and any distinct notification type or workflow for regressions vs new issues.", "evidenceIds": [ "rollbar-docs-12", "rollbar-docs-32", "rollbar-docs-6", "rollbar-docs-7" ] }, { "productId": "rollbar", "storyId": "release-health-crash-free", "verdict": "partial", "quality": 3, "confidence": "medium", "rationale": "Rollbar offers version/deploy tracking so you can see which code version is throwing errors (rollbar-docs-6, rollbar-docs-7) and pricing mentions 'sessions' as a metered unit (rollbar-docs-21), but there is no evidence of a dedicated release-health dashboard with crash-free session/user percentages or a canary-vs-stable comparison view. Missing for 10: crash-free session/user rate metrics, adoption-per-release charts, and canary-vs-stable comparison UI.", "evidenceIds": [ "rollbar-docs-6", "rollbar-docs-7", "rollbar-docs-21" ] }, { "productId": "rollbar", "storyId": "release-tracking-commits", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Rollbar's docs confirm version/release tracking (seeing which code versions throw exceptions) and deploy tracking that unlocks debugging features tied to releases, plus source map support to map errors back to original code. However, there is no explicit evidence of automatic 'suspect commit' identification or a UI showing which specific commit likely caused an issue — only general deploy/version association is documented. missing for 10: explicit suspect-commit/blame detection linking an item to a specific commit, first-appeared-in-release UI element, and independent/hands-on confirmation of this workflow.", "evidenceIds": [ "rollbar-docs-6", "rollbar-docs-7", "rollbar-docs-28", "rollbar-docs-5", "rollbar-docs-40" ] }, { "productId": "rollbar", "storyId": "scm-blame-context", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows only generic source-map based stack traces (file/line/method) and GitHub credentials used for unspecified 'third-party integrations,' but nothing about suspect-commit detection, code owner attribution, or deep-linking from a stack frame to the exact source line on a default branch in a connected repo.", "evidenceIds": [ "rollbar-docs-5", "rollbar-docs-27", "rollbar-comm-4" ] }, { "productId": "rollbar", "storyId": "sdk-broad-coverage", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Rollbar documents official SDKs across a dozen+ platforms (rollbar.js for web frontend/backend frameworks like React, Angular, Express, Next.js; Ruby, PHP/Magento2, etc.) that automatically capture uncaught errors and exceptions, with telemetry, source map support, and person/version tracking. Cross-platform coverage including client and server-side JS is explicitly confirmed via GitHub repo description. Missing for 10: explicit first-party documentation naming mobile-specific SDKs (iOS/Android) with auto-capture details, and independent hands-on verification of automatic uncaught-exception capture across all platforms.", "evidenceIds": [ "rollbar-docs-1", "rollbar-gh-4", "rollbar-gh-1", "rollbar-docs-5", "rollbar-docs-44", "rollbar-docs-39" ] }, { "productId": "rollbar", "storyId": "server-side-data-scrubbing", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack contains no mention of PII scrubbing, data scrubbing rules, or SDK-level filtering of sensitive fields anywhere in the docs, GitHub, or community citations. While this is a plausible and expected capability for an error-monitoring SaaS like Rollbar, none of the provided evidence documents server-side scrubbing rules or client SDK filtering features.", "evidenceIds": [] }, { "productId": "rollbar", "storyId": "user-feedback-on-errors", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Rollbar's evidence covers error grouping, telemetry, person tracking, session replay, and notifications, but there is no mention of a crash-report dialog or feedback widget that attaches end-user feedback to a specific error occurrence.", "evidenceIds": [] }, { "productId": "sentry", "storyId": "affected-users-quantified", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack covers issue triage, alerts, ownership rules, AI debugging (Seer), and SDK/CLI capabilities, but nothing documents quantifying 'users affected' or 'sessions affected' per issue for blast-radius prioritization. Without any citation on impact-based metrics tied to issues, this capability is unevidenced in the pack.", "evidenceIds": [] }, { "productId": "sentry", "storyId": "agent-triages-errors", "verdict": "partial", "quality": 7, "confidence": "medium", "rationale": "Sentry ships both an official MCP server (mcp.sentry.dev) for searching errors, triaging issues, and managing projects, and REST APIs (e.g., bulk mutate/list project issues) that could surface top production issues and stack traces; it also documents connecting AI coding agents so Seer's root-cause analysis can be sent to the agent for automated fix generation and PR creation. This covers pull+triage+file-a-fix workflows well, but evidence is oriented toward filing PRs into a coding agent/repo rather than explicitly filing tickets into a separate issue tracker (Jira/Linear), and no example shows an agent pulling stack traces specifically via API in one documented flow. missing for 10: explicit documented flow of pulling stack traces via API for 'top issues', and explicit filing into a third-party issue tracker (not just PR creation).", "evidenceIds": [ "sentry-docs-8", "sentry-docs-23", "sentry-docs-38", "sentry-docs-41", "sentry-probe-3" ] }, { "productId": "sentry", "storyId": "agentic-agent-docs", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "A direct probe confirms Sentry serves an llms.txt file at docs.sentry.io/llms.txt with structured documentation content, and Sentry also documents agent-oriented workflows (agents.toml skill dependencies, agent plugin docs, one-command setup for Claude Code/Cursor/Codex/Grok). Missing for 10: independent/community confirmation that an agent successfully consumed the llms.txt or agent-oriented docs end-to-end.", "evidenceIds": [ "sentry-probe-1", "sentry-docs-4", "sentry-docs-39", "sentry-docs-57" ] }, { "productId": "sentry", "storyId": "agentic-ai-insights", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Sentry ships Seer, an AI debugging agent that performs root-cause analysis, autofix code suggestions, and code review directly within the product (sentry-docs-2, sentry-docs-13, sentry-docs-26, sentry-docs-40), plus agent tracing that surfaces insights on tool calls/spend (sentry-docs-3, sentry-docs-42). This is gated behind a paid subscription and documented mainly via first-party docs/changelog rather than deep independent hands-on validation. Missing for 10: independent/community hands-on verification of Seer's suggestion quality and broader third-party corroboration beyond vendor docs.", "evidenceIds": [ "sentry-docs-2", "sentry-docs-13", "sentry-docs-26", "sentry-docs-40", "sentry-docs-3", "sentry-docs-42", "sentry-docs-59" ] }, { "productId": "sentry", "storyId": "agentic-autonomous-automation", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Sentry's Seer/Autofix agent autonomously root-causes issues and can even generate PRs via connected coding agents (sentry-docs-2, 13, 26, 40, 41), and alerting/cron monitoring run in the background without manual triggering (sentry-docs-17, 35, 50). However this autonomy is issue-triggered rather than a general-purpose scheduler, gated behind a paid subscription (sentry-docs-59), and there is no independent/hands-on evidence validating how reliably it runs unattended. Missing for 10: independent corroboration of autonomous Seer/Autofix runs, evidence of broader scheduled/background automation beyond issue-triggered debugging, and confirmation it works without subscription.", "evidenceIds": [ "sentry-docs-2", "sentry-docs-13", "sentry-docs-26", "sentry-docs-40", "sentry-docs-41", "sentry-docs-42", "sentry-docs-59", "sentry-docs-35", "sentry-docs-50" ] }, { "productId": "sentry", "storyId": "agentic-builtin-assistant", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Sentry ships Seer, a built-in AI debugging agent/code reviewer directly inside the product that can autonomously root-cause issues, suggest fixes, and be delegated tasks like 'just ask' for debugging, alerts, or setup (sentry-docs-2, sentry-docs-26, sentry-docs-39, sentry-docs-40). This is a first-party, in-product AI assistant, not just an external agent connector. Missing for 10: independent/hands-on user corroboration of Seer's actual task-delegation quality, and note that full Seer access requires a paid subscription (sentry-docs-59).", "evidenceIds": [ "sentry-docs-2", "sentry-docs-13", "sentry-docs-26", "sentry-docs-39", "sentry-docs-40", "sentry-docs-59" ] }, { "productId": "sentry", "storyId": "agentic-headless", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Sentry has a documented CLI (sentry-cli) for sending events, uploading source maps, managing releases, crons, and debug files, which supports CI/headless automation, plus SDKs that integrate into CI/build pipelines. However, evidence is scattered docs-references rather than an explicit 'run Sentry in CI/headlessly' guide or CI integration examples, and no independent hands-on confirmation of CI usage exists. missing for 10: explicit CI/automation guide or pipeline examples, independent verification of headless CI usage, and clarity on which features (Seer, MCP) work outside interactive/agent contexts.", "evidenceIds": [ "sentry-docs-24", "sentry-docs-52", "sentry-docs-56", "sentry-docs-50", "sentry-probe-4", "sentry-docs-15" ] }, { "productId": "sentry", "storyId": "agentic-mcp-client", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows Sentry ships its own MCP server (mcp.sentry.dev) so other AI assistants can call Sentry's tools, and it can even monitor/observe MCP server traffic (docs-44) — but this is the reverse direction. There is no evidence that Sentry itself (or its Seer agent) can act as an MCP client and plug in external MCP servers to use their tools.", "evidenceIds": [ "sentry-docs-8", "sentry-probe-3", "sentry-docs-41", "sentry-docs-44" ] }, { "productId": "sentry", "storyId": "agentic-mcp-server", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Sentry publishes an official MCP server at mcp.sentry.dev enabling AI agents to search errors, analyze performance, triage issues, and manage projects via the Model Context Protocol, corroborated by docs pointing users to set it up alongside the Sentry CLI. Missing for 10: independent/hands-on community verification of the MCP server working in practice, and more detail on setup reliability.", "evidenceIds": [ "sentry-docs-8", "sentry-docs-5", "sentry-probe-3", "sentry-docs-4" ] }, { "productId": "sentry", "storyId": "agentic-nl-commands", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Sentry ships an official MCP server plus an 'agent plugin' explicitly designed so users can operate Sentry via natural language ('just ask' to add a project, debug errors, or wire up alerts), and the MCP server supports searching errors, triaging issues, and managing projects conversationally. Missing for 10: independent/hands-on user reports confirming natural-language reliability, and no evidence of a native chat/command interface outside the MCP+agent integration path.", "evidenceIds": [ "sentry-docs-39", "sentry-docs-8", "sentry-docs-5", "sentry-docs-41", "sentry-probe-3" ] }, { "productId": "sentry", "storyId": "agentic-official-cli", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Sentry ships and documents an official sentry-cli tool (docs.sentry.io/cli) with commands for releases, debug files, sending events, streaming logs, and CI integration, plus a documented flow for AI agents to install/configure Sentry via one command. missing for 10: no independent/hands-on corroboration of CLI use specifically in AI-native/agentic workflows beyond vendor docs.", "evidenceIds": [ "sentry-docs-4", "sentry-docs-24", "sentry-docs-52", "sentry-docs-53", "sentry-docs-56", "sentry-probe-4" ] }, { "productId": "sentry", "storyId": "agentic-public-api", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Sentry documents a public REST API (e.g. issue-listing/mutation endpoints) plus a CLI and SDKs that let programmatic/AI-driven clients interact with the platform, and pricing explicitly lists 'API & third-party integrations' as a tier feature. However, probes for a formal OpenAPI/swagger spec all 404'd, so there's no machine-readable schema confirmed, and independent hands-on corroboration of API usage is absent. Missing for 10: a discoverable OpenAPI/swagger spec, independent developer confirmation of API usage.", "evidenceIds": [ "sentry-docs-23", "sentry-docs-11", "sentry-docs-24", "sentry-probe-2", "sentry-probe-4" ] }, { "productId": "sentry", "storyId": "agentic-scoped-keys", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack shows Sentry's general API access, MCP server, and SAML/SCIM support, but nothing about issuing scoped or least-privilege API credentials specifically for AI agents (e.g., token scopes, agent-specific permission grants). No documentation or hands-on evidence addresses credential scoping for agent use.", "evidenceIds": [] }, { "productId": "sentry", "storyId": "agentic-sdks", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Sentry documents official SDKs across many languages/platforms (docs-45–49) and explicitly extends these into AI-native workflows via agent tracing/instrumentation docs (docs-42, docs-44), an official MCP server (docs-8, sentry-probe-3), a coding-agent integration guide (docs-41), and one-command setup for Claude Code/Cursor/Codex/Grok (docs-4). This directly supports building against official SDKs in an agentic/AI-native context. Missing for 10: independent/hands-on developer corroboration of AI-native SDK integration specifically, and no explicit versioned SDK API reference cited beyond platform pages.", "evidenceIds": [ "sentry-docs-4", "sentry-docs-8", "sentry-docs-41", "sentry-docs-42", "sentry-docs-44", "sentry-docs-45", "sentry-docs-46", "sentry-probe-3" ] }, { "productId": "sentry", "storyId": "agentic-webhooks", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Sentry has a dedicated Webhooks integration-platform doc describing subscribing to events via webhooks, which is a documented first-party capability usable by AI-native/automation consumers alongside its API and MCP surfaces. missing for 10: independent/hands-on corroboration of webhook reliability and no explicit example of an AI agent consuming these webhooks directly.", "evidenceIds": [ "sentry-docs-25" ] }, { "productId": "sentry", "storyId": "ai-fix-pull-request", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Sentry's Seer/Autofix explicitly generates root-cause analysis and code fixes, and docs describe connecting an AI coding agent so Seer's analysis is sent to the agent for automated fix generation and pull request creation (sentry-docs-41, sentry-docs-40, sentry-docs-26, sentry-docs-13). This is a first-party documented workflow matching the story closely. Missing for 10: independent/hands-on confirmation that PR creation works reliably end-to-end and more detail on human review flow.", "evidenceIds": [ "sentry-docs-41", "sentry-docs-40", "sentry-docs-26", "sentry-docs-13", "sentry-docs-27" ] }, { "productId": "sentry", "storyId": "ai-root-cause", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Sentry's Seer/Autofix explicitly analyzes stack traces, breadcrumbs, and suspect commits to automatically root-cause issues and generate suggested fixes, matching the story closely (sentry-docs-13, sentry-docs-26, sentry-docs-40, sentry-docs-21). This is documented as a first-party feature with a dedicated product page, though gated behind a paid subscription. missing for 10: independent/hands-on validation of root-cause accuracy, and explicit confirmation that breadcrumbs are directly cited in Seer's output (only inferred from feature overlap).", "evidenceIds": [ "sentry-docs-13", "sentry-docs-26", "sentry-docs-40", "sentry-docs-21", "sentry-docs-59", "sentry-docs-41" ] }, { "productId": "sentry", "storyId": "alert-routing-channels", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Sentry documents Alerts as a first-class feature with native Slack and PagerDuty integrations, email notifications (per pricing docs), and Ownership Rules for project-based routing — covering the core of alert routing by channel and project. Missing for 10: explicit documentation of the specific threshold types named in the story (new issue, frequency spike, affected-user count) and severity-tag-based routing rules, plus independent/hands-on corroboration beyond doc titles.", "evidenceIds": [ "sentry-docs-17", "sentry-docs-18", "sentry-docs-19", "sentry-docs-22", "sentry-docs-10", "sentry-docs-32" ] }, { "productId": "sentry", "storyId": "api-interactive-docs", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Sentry's docs reference API endpoints (e.g., sentry-docs-23) but there is no evidence of an interactive, runnable API reference (like Swagger/OpenAPI try-it-out); a probe for OpenAPI/Swagger specs returned 404s, suggesting no such interactive tooling is exposed.", "evidenceIds": [ "sentry-docs-23", "sentry-probe-2" ] }, { "productId": "sentry", "storyId": "api-machine-spec", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Sentry's docs reference REST API endpoints, but a direct probe for a machine-readable OpenAPI/Swagger spec at the standard docs.sentry.io locations returned 404 for all candidates, and no other evidence pack item shows a downloadable spec file. Missing for 10: any linked OpenAPI/Swagger JSON or YAML file, any docs page announcing spec availability.", "evidenceIds": [ "sentry-probe-2", "sentry-docs-23" ] }, { "productId": "sentry", "storyId": "api-sandbox", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence of a sandbox/staging environment or test mode that isolates AI-native testing from production data; self-hosted option (sentry-docs-28) allows separate deployments but that's not documented as a sandbox for AI agent testing.", "evidenceIds": [] }, { "productId": "sentry", "storyId": "api-versioning-policy", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence of a documented API versioning scheme or deprecation policy; the OpenAPI spec probe returned 404s and no docs mention API version numbers or deprecation timelines.", "evidenceIds": [ "sentry-probe-2" ] }, { "productId": "sentry", "storyId": "automation-bulk-operations", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Sentry's API and CLI provide bulk operation support — the API explicitly documents 'Bulk Mutate a List of Issues' and the CLI supports scripted release/artifact/event operations, plus an MCP server exposes issue-management tools to AI agents. However, there's no dedicated evidence of AI-native bulk workflows (e.g., an agent bulk-triaging/resolving many issues in one MCP call) beyond individual issue-level MCP actions. Missing for 10: explicit documentation of MCP/agent-driven bulk operations across many items, and independent/hands-on confirmation of bulk API usage at scale.", "evidenceIds": [ "sentry-docs-23", "sentry-docs-8", "sentry-probe-3", "sentry-docs-56", "sentry-docs-52" ] }, { "productId": "sentry", "storyId": "automation-rules-engine", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Sentry supports rule-based automation like Alerts, Ownership Rules, Fingerprint Rules, and Webhooks that trigger notifications/actions (Slack, PagerDuty) on events, plus Autofix/Seer for automated root-cause and PR generation triggered by issues. However, this is more traditional alerting/ownership routing than a unified 'AI-native automation rules engine' with conditional logic tailored for agentic workflows. missing for 10: evidence of a dedicated rule-builder for AI-agent-triggered actions, complex conditional/multi-step automation workflows, and independent verification of these rules working reliably in practice.", "evidenceIds": [ "sentry-docs-17", "sentry-docs-22", "sentry-docs-14", "sentry-docs-18", "sentry-docs-19", "sentry-docs-25", "sentry-docs-26", "sentry-docs-41" ] }, { "productId": "sentry", "storyId": "automation-scheduled-jobs", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Sentry's 'Cron Monitoring' (sentry-docs-35, sentry-docs-50) only tracks the health/uptime of recurring jobs that run elsewhere — it does not let a user actually schedule, define, or execute recurring jobs or AI workflows within Sentry itself. No evidence shows Sentry offering job/workflow scheduling or orchestration capability for AI-native automation.", "evidenceIds": [ "sentry-docs-35", "sentry-docs-50" ] }, { "productId": "sentry", "storyId": "automation-versioned-workflows", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Sentry documents various rule-based automations (alerts, ownership rules, fingerprint rules) and AI agent features (Seer, Autofix), but there is no evidence of version history, review workflows, or rollback capabilities for these automations themselves — only for code releases/commits, which is a different axis. missing for 10: version history for automation rules, review/approval flow for automation changes, rollback mechanism for AI agent actions or rule configurations.", "evidenceIds": [] }, { "productId": "sentry", "storyId": "breadcrumbs-custom-context", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack confirms Sentry SDKs exist and capture errors/traces (e.g., sentry-docs-1, sentry-docs-45, sentry-docs-46), but nothing in the pack specifically documents breadcrumbs, custom tags, or user context attachment APIs. Without direct evidence of these instrumentation primitives, the story cannot be credited as delivered per the evidence-only rule.", "evidenceIds": [] }, { "productId": "sentry", "storyId": "error-grouping-dedup", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Sentry documents Fingerprint Rules for customizing grouping when automatic stack-trace grouping gets it wrong, and issue-status/triage docs support the workflow. However, evidence lacks detail on the core automatic fingerprinting algorithm itself (how thousands of duplicates are merged) and there's no independent/hands-on corroboration of grouping accuracy or the customization UX working well in practice. Missing for 10: deeper docs/evidence on default grouping algorithm, merge/split issue UI, and independent validation of grouping quality.", "evidenceIds": [ "sentry-docs-14", "sentry-docs-20", "sentry-docs-22" ] }, { "productId": "sentry", "storyId": "error-trend-dashboards", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Pricing pages confirm Sentry ships 'custom dashboards' (10 on free tier, 20 on team tier) and release/issue tracking features like 'Associate Commits' and 'Issue Status', implying some cross-project reporting capability, but no evidence describes dashboard content such as top regressions, new-issues-per-release, or volume-over-time views, nor cross-team rollups for engineering leaders. Missing for 10: documentation of specific dashboard widgets/views (regression detection, release comparison, volume trends), evidence of team/org-level rollups, and any hands-on or independent confirmation of these analytics views.", "evidenceIds": [ "sentry-docs-10", "sentry-docs-11", "sentry-docs-16", "sentry-docs-20", "sentry-docs-32" ] }, { "productId": "sentry", "storyId": "event-search-queries", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack contains no documentation or claims about Sentry's issue search/query syntax (e.g., filtering by tag, release, environment, or custom properties across projects). Items like 'Bulk Mutate a List of Issues' or 'Issue Status' touch issue management but never describe a query language or cross-project filtering capability.", "evidenceIds": [] }, { "productId": "sentry", "storyId": "issue-tracker-sync", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack covers issue status, suspect commits, ownership rules, webhooks, Slack/PagerDuty notifications, and AI/MCP integrations, but contains no mention of Jira, GitHub Issues, or Linear integrations, nor any description of two-way status sync between Sentry issues and external trackers.", "evidenceIds": [] }, { "productId": "sentry", "storyId": "issue-triage-workflow", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Sentry's docs explicitly cover issue triage lifecycle (states-triage doc), ownership/assignment rules, and bulk mutation APIs for resolving/ignoring issues, which are core, well-known Sentry features including automatic reopening of resolved issues on regression. However the evidence pack only cites doc titles/URLs rather than quoting the actual regression-reopen and snooze mechanics in detail. Missing for 10: explicit doc text confirming auto-reopen-on-regression behavior and snooze/ignore duration options, plus independent hands-on confirmation of this specific triage flow.", "evidenceIds": [ "sentry-docs-20", "sentry-docs-22", "sentry-docs-23", "sentry-docs-21" ] }, { "productId": "sentry", "storyId": "issues-api-read", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Docs confirm a documented REST API for listing and bulk-mutating issues (sentry-docs-23) and general API/integrations access is called out in pricing (sentry-docs-11), but the pack lacks explicit evidence of scoped API tokens or a raw-event fetch endpoint, and an OpenAPI spec probe returned 404s (sentry-probe-2). missing for 10: explicit scoped-token/auth documentation, raw event retrieval endpoint evidence, and a working machine-readable API spec.", "evidenceIds": [ "sentry-docs-23", "sentry-docs-11", "sentry-probe-2" ] }, { "productId": "sentry", "storyId": "js-sourcemap-unminify", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Sentry docs explicitly cover uploading JavaScript source maps (sentry-docs-15), sentry-cli's role in release/source map management and code mappings (sentry-docs-6, sentry-docs-24, sentry-docs-55, sentry-docs-56), which together describe uploading source maps via CLI/CI to deobfuscate stack traces. Missing for 10: explicit bundler-plugin (webpack/vite) documentation citation and independent/hands-on confirmation that stack traces are correctly symbolicated in production.", "evidenceIds": [ "sentry-docs-15", "sentry-docs-6", "sentry-docs-24", "sentry-docs-55", "sentry-docs-56", "sentry-probe-4" ] }, { "productId": "sentry", "storyId": "merge-split-issues", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Sentry's Fingerprint Rules doc shows a mechanism to influence/correct how events are grouped (addressing mis-grouping), but there is no evidence in the pack of an explicit merge/unmerge issues action or UI, which is the core mechanic described in the story. Missing for 10: explicit merge-issues and unmerge/split-issues documentation or feature reference, hands-on confirmation of the merge/split workflow.", "evidenceIds": [ "sentry-docs-14", "sentry-docs-20" ] }, { "productId": "sentry", "storyId": "mobile-crash-reporting", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Sentry documents Android SDK and debug information files (dSYMs/proguard) for symbolicating native crashes, plus sentry-cli support for managing debug info files for iOS/Android, which underpins NDK/native crash reporting with device/OS/app-version context. However, there is no explicit iOS or NDK-specific SDK documentation excerpt, and no direct evidence of the captured context fields (device, OS version, app version) shown in a crash report. missing for 10: explicit iOS SDK docs, NDK-specific crash capture documentation, evidence of device/OS/app-version context fields in captured events, and independent/hands-on confirmation of native crash symbolication working.", "evidenceIds": [ "sentry-docs-46", "sentry-docs-51", "sentry-docs-24", "sentry-docs-6" ] }, { "productId": "sentry", "storyId": "native-symbolication", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Sentry docs explicitly describe managing debug information files for iOS/Android (dSYM, ProGuard, native symbols) via sentry-cli, and dedicated docs confirm CLI upload of debug information files enables stack trace symbolication for compiled platforms. Community evidence corroborates real-world use of Sentry's symbolication engine (Symbolicator) being adopted by competitors, validating the capability's maturity. Missing for 10: independent hands-on verification specifically of dSYM/ProGuard upload workflows and confirmation of accurate line-level symbolication outcomes.", "evidenceIds": [ "sentry-docs-6", "sentry-docs-24", "sentry-docs-51", "sentry-docs-56", "sentry-comm-8", "sentry-probe-4" ] }, { "productId": "sentry", "storyId": "noise-spike-controls", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Sentry docs cover alerting (sentry-docs-17), issue mute/ignore/resolve states (sentry-docs-20), and quota/rate-limit management (sentry-docs-30), which together support noise-reduction workflows for SREs. However, there is no explicit documentation of 'spike protection' as a named feature or per-key/per-DSN rate limiting controls, only general billing quotas. Missing for 10: explicit spike protection docs, per-key rate limit configuration, and independent/hands-on validation that these reduce night-time paging.", "evidenceIds": [ "sentry-docs-17", "sentry-docs-20", "sentry-docs-30", "sentry-docs-22" ] }, { "productId": "sentry", "storyId": "open-ingest-protocol", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Evidence shows sentry-cli can send events directly (sentry-docs-52) and there is a large SDK ecosystem plus community-supported SDKs (sentry-docs-45), implying an underlying ingestion protocol exists beyond official SDKs, but there is no dedicated public specification of the event/envelope protocol, no OpenAPI spec (probe found 404s at sentry-docs/openapi paths), and no first-party documentation explicitly inviting third parties to build custom clients against a documented wire protocol. missing for 10: a published open protocol/spec for event ingestion (e.g., envelope format documentation), explicit statement that custom/unofficial clients are supported, and independent evidence of third-party clients successfully using the raw protocol.", "evidenceIds": [ "sentry-docs-52", "sentry-docs-45", "sentry-probe-2" ] }, { "productId": "sentry", "storyId": "openness-api-parity", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Sentry documents a broad REST API (issue management, releases, alerts, webhooks) and an MCP server/CLI for AI-agent access, showing substantial API coverage, but there's no explicit claim or evidence of full UI/API parity, and probes for a discoverable OpenAPI spec all 404'd, suggesting gaps in comprehensive API documentation. missing for 10: explicit full UI/API parity statement, discoverable OpenAPI/swagger spec, evidence covering less common UI features (dashboards, ownership rules, org settings) via API.", "evidenceIds": [ "sentry-docs-23", "sentry-docs-8", "sentry-docs-38", "sentry-docs-11", "sentry-probe-2", "sentry-probe-3" ] }, { "productId": "sentry", "storyId": "openness-full-export", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Sentry is open source and can be fully self-hosted (sentry-docs-28, sentry-comm-2), and it exposes API endpoints to list/mutate issues (sentry-docs-23) plus a CLI for releases/events (sentry-docs-52, sentry-docs-56), giving technical means to extract data and avoid lock-in. However, there is no documented bulk/full-account data export feature or open-format export guarantee, and a community comment even cites a competitor's 'no vendor lock-in' pitch as a differentiator from Sentry (sentry-comm-4), suggesting this isn't a headline capability. Missing for 10: an explicit data-export/portability doc, documented export formats, and confirmation of no proprietary lock-in for hosted users.", "evidenceIds": [ "sentry-docs-28", "sentry-docs-23", "sentry-docs-52", "sentry-docs-56", "sentry-comm-2", "sentry-comm-4" ] }, { "productId": "sentry", "storyId": "openness-open-license", "verdict": "disputed", "quality": 5, "confidence": "medium", "rationale": "Sentry's source is publicly visible on GitHub (sentry-gh-1, sentry-gh-2) and self-hosting docs (sentry-docs-28) reference running 'all of Sentry' for free, with community praise for the 'fully open source nature' (sentry-comm-1). However, community evidence directly contradicts a fully open license: a user states they 'refuse to rely on closed-source software' after a licensing change (sentry-comm-3), and another notes 'not seeing what looks like too many, if any, outside contributions' (sentry-comm-8), indicating Sentry moved to a source-available/BSL-style license rather than a traditional OSI open license. Missing for 10: explicit documentation of the current license terms, confirmation of OSI-approved status, and resolution of the community-reported licensing shift.", "evidenceIds": [ "sentry-gh-1", "sentry-gh-2", "sentry-docs-28", "sentry-comm-1", "sentry-comm-3", "sentry-comm-8" ] }, { "productId": "sentry", "storyId": "openness-self-host", "verdict": "partial", "quality": 7, "confidence": "medium", "rationale": "Sentry officially documents running the full core product self-hosted for free (sentry-docs-28, sentry-docs-9), and community reports confirm it works well long-term (sentry-comm-2). However, real caveats exist: some users report heavy resource consumption when self-hosting (sentry-comm-5) and there is documented community frustration over licensing changes limiting true open-source freedom (sentry-comm-3), which matters for the openness theme. Missing for 10: independent verification that current license terms fully satisfy open-source expectations, and evidence addressing the resource/scaling complaints.", "evidenceIds": [ "sentry-docs-28", "sentry-docs-9", "sentry-comm-2", "sentry-comm-5", "sentry-comm-3" ] }, { "productId": "sentry", "storyId": "privacy-data-residency", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Sentry docs explicitly document 'Data Storage Location (US or EU)' as a configurable option, and self-hosted deployment is also available for full data residency control. Missing for 10: independent/hands-on corroboration of the region-selection workflow and details on which plans/regions are supported.", "evidenceIds": [ "sentry-docs-31", "sentry-docs-28", "sentry-comm-2" ] }, { "productId": "sentry", "storyId": "privacy-no-training", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence of any policy, setting, or statement addressing whether user data is used to train AI models (Sentry's own or third-party AI features like Seer/Autofix). Self-hosting and data scrubbing docs exist but don't address AI training use specifically.", "evidenceIds": [] }, { "productId": "sentry", "storyId": "privacy-retention-controls", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Sentry documents some privacy-adjacent controls like server-side PII scrubbing and choice of data storage location (US/EU), which touch on data governance, but the evidence pack has no explicit documentation of configurable data retention periods, deletion APIs, or GDPR-style delete-my-data workflows. Missing for 10: explicit retention period settings, data deletion/erasure API or UI, documentation of default retention windows and how AI-native users can programmatically purge stored error/trace data.", "evidenceIds": [ "sentry-docs-29", "sentry-docs-31", "sentry-docs-30" ] }, { "productId": "sentry", "storyId": "privacy-telemetry-optout", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence pack items address telemetry/usage-tracking opt-out settings for Sentry itself (e.g., product analytics or CLI telemetry opt-out flags); only data scrubbing and self-hosted options are mentioned, which is a different concern than telemetry opt-out.", "evidenceIds": [] }, { "productId": "sentry", "storyId": "quota-spend-controls", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Sentry documents 'Billing Quota Management' and 'Advanced quota management' as pricing-tier features, indicating per-project event quotas and spend controls exist, but the evidence pack lacks detail on how spend caps are configured, alerting on quota thresholds, or hard-stop billing guarantees. missing for 10: detailed docs/UI walkthrough of setting spend limits, independent/hands-on confirmation that caps reliably prevent overage billing, and clarity on per-project (vs org-wide) granularity.", "evidenceIds": [ "sentry-docs-30", "sentry-docs-58" ] }, { "productId": "sentry", "storyId": "regression-alerts", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Sentry's docs reference an 'Issue Status' page (states-triage) that implies distinct regression tracking, and separate Alerts documentation, but the evidence pack provides only page titles with no substantive text confirming a specific 'regression' alert type distinct from new-issue alerts. missing for 10: explicit documentation or quotes describing regression detection/alerting logic, confirmation that regression alerts are routed separately from new-issue alerts, and any hands-on/community validation of this specific behavior.", "evidenceIds": [ "sentry-docs-20", "sentry-docs-17", "sentry-docs-16" ] }, { "productId": "sentry", "storyId": "release-health-crash-free", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack includes only generic release docs (Associate Commits, releases.md CLI page) but no mention of crash-free sessions/users metrics, adoption tracking, or canary vs stable release comparison — the specific 'release health' capability described in the story is unevidenced.", "evidenceIds": [ "sentry-docs-16", "sentry-docs-56" ] }, { "productId": "sentry", "storyId": "release-tracking-commits", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Sentry docs explicitly cover 'Associate Commits' and 'Suspect Commits' features tying issues to releases and the likely-causing commit, backed by supporting infra like source maps, code mappings, and CLI release/commit association tooling. Missing for 10: independent hands-on corroboration of suspect-commit accuracy and no detailed walkthrough of the release timeline UI in this pack.", "evidenceIds": [ "sentry-docs-16", "sentry-docs-21", "sentry-docs-55", "sentry-docs-56", "sentry-docs-15" ] }, { "productId": "sentry", "storyId": "scm-blame-context", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Sentry documents Suspect Commits, Ownership Rules (code owners), and code mappings that link stack trace paths to source repo paths enabling source context, suspect commits, stack trace linking, and code owners — directly matching the story. Missing for 10: no independent/hands-on corroboration of the exact 'jump to default branch line' UX or repo integration setup friction.", "evidenceIds": [ "sentry-docs-21", "sentry-docs-22", "sentry-docs-55", "sentry-docs-16" ] }, { "productId": "sentry", "storyId": "sdk-broad-coverage", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "Sentry documents SDKs for a broad range of platforms (Android, .NET, Elixir, Dart, JavaScript, etc.) that automatically capture errors/exceptions with minimal setup, plus a general platform-support claim ('if you use it, we probably support it') covering web, backend, and mobile. Missing for 10: independent hands-on confirmation of automatic uncaught-error capture across all listed SDKs (only first-party docs cited).", "evidenceIds": [ "sentry-docs-1", "sentry-docs-7", "sentry-docs-45", "sentry-docs-46", "sentry-docs-47", "sentry-docs-48", "sentry-docs-49", "sentry-docs-12" ] }, { "productId": "sentry", "storyId": "server-side-data-scrubbing", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Sentry documents server-side data scrubbing rules (sentry-docs-29) which directly matches the SRE story, and SDK-level filtering (beforeSend/scrubbing hooks) is a known Sentry SDK feature area implied by platform SDK docs, but the evidence pack lacks explicit documentation of SDK-level filtering callbacks or confirmation that scrubbing prevents sensitive payloads from ever persisting server-side (vs. being scrubbed post-ingestion). missing for 10: explicit SDK-level PII filtering/beforeSend docs, confirmation of pre-persistence scrubbing guarantees, independent validation of scrubbing effectiveness.", "evidenceIds": [ "sentry-docs-29" ] }, { "productId": "sentry", "storyId": "user-feedback-on-errors", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack covers SDKs, error monitoring, tracing, Seer AI, MCP, CLI, and integrations, but contains no mention of a user feedback widget, crash-report dialog, or any mechanism for collecting end-user feedback tied to a specific error event. Missing for 10: any documentation of a feedback widget/crash dialog feature, API for attaching feedback to an event, or configuration guide for it.", "evidenceIds": [] } ]