[ { "productId": "canny", "storyId": "agentic-agent-docs", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "A direct probe confirms llms.txt exists and returns 200 with developer documentation content at developers.canny.io/llms.txt, and Canny also publishes agent-oriented docs (MCP server docs, API reference) explicitly designed for LLM/agent consumption. Missing for 10: independent third-party confirmation that agents successfully consume this llms.txt in practice.", "evidenceIds": [ "canny-probe-1", "canny-docs-15", "canny-docs-16", "canny-probe-3" ] }, { "productId": "canny", "storyId": "agentic-ai-insights", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Canny provides an official MCP server enabling users to ask their LLM for insights (top themes, ARR impact, roadmap queries) directly from feedback data, and to act on it (update statuses, draft PRDs, run agents) — matching the AI-native insight-generation story. missing for 10: no independent/hands-on validation of insight quality or accuracy, and no first-party in-app AI analytics dashboard beyond the MCP-based LLM interaction.", "evidenceIds": [ "canny-docs-4", "canny-docs-5", "canny-docs-9", "canny-docs-10", "canny-probe-3" ] }, { "productId": "canny", "storyId": "agentic-autonomous-automation", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Canny's Autopilot feature automatically captures feedback from Gong, Intercom, Slack, Fathom, Fireflies, and review platforms without manual triggering, and native integrations (Jira, Linear, GitHub, etc.) keep statuses synced automatically — these qualify as background automations. However, this is limited to feedback ingestion/sync rather than a general-purpose agentic automation framework the user can configure for arbitrary autonomous tasks, and the MCP-based actions (updating statuses, replying to customers) appear to require active LLM invocation rather than running unattended in the background. Missing for 10: evidence of user-configurable, scheduled/triggered autonomous workflows beyond feedback capture, and confirmation that MCP-driven actions can run without a live agent session.", "evidenceIds": [ "canny-docs-6", "canny-docs-7", "canny-docs-11", "canny-intdir-2", "canny-intdir-3", "canny-intdir-4", "canny-intdir-5", "canny-intdir-6" ] }, { "productId": "canny", "storyId": "agentic-builtin-assistant", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Canny ships 'Autopilot,' an automated AI feature that captures and categorizes feedback from support/sales/review sources without user intervention, and users can 'send new conversations to Autopilot' to have it process feedback — a limited form of task delegation to a built-in AI component. However, this is not a general-purpose conversational assistant embedded in the Canny UI; the richer AI interaction (asking questions, taking actions, drafting PRDs) is delivered via MCP connecting to *external* LLMs like ChatGPT/Claude/Cursor, not a native in-product assistant. Missing for 10: evidence of an interactive, conversational built-in assistant UI, an example of delegating arbitrary multi-step tasks to it, and independent/hands-on confirmation of Autopilot's task-delegation behavior beyond marketing copy.", "evidenceIds": [ "canny-docs-6", "canny-docs-7", "canny-docs-11", "canny-intdir-2", "canny-intdir-3" ] }, { "productId": "canny", "storyId": "agentic-headless", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Canny exposes a REST API reference and API-driven install/SDK docs, which implies some programmatic/automatable access, but there is no explicit documentation of CI usage, a CLI, or headless automation workflows; the MCP server is described as requiring interactive secure sign-in rather than API-key/CI-friendly auth. missing for 10: explicit CI/headless usage examples, CLI or scriptable automation docs, non-interactive auth for MCP/API use in pipelines.", "evidenceIds": [ "canny-docs-16", "canny-docs-3", "canny-probe-2" ] }, { "productId": "canny", "storyId": "agentic-mcp-client", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "All evidence shows Canny exposes an MCP *server* that external AI tools (ChatGPT, Claude, Cursor) connect to — the reverse direction. There is no evidence that Canny itself acts as an MCP client that plugs in external MCP servers to use their tools.", "evidenceIds": [ "canny-docs-3", "canny-docs-9", "canny-docs-13", "canny-probe-3" ] }, { "productId": "canny", "storyId": "agentic-mcp-server", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Canny documents an official MCP server that connects to ChatGPT, Claude, and Cursor via secure sign-in (no API keys), enabling agents to query feedback data and take actions like updating statuses and replying to customers. Missing for 10: independent/hands-on third-party corroboration beyond vendor docs.", "evidenceIds": [ "canny-docs-3", "canny-docs-4", "canny-docs-5", "canny-docs-9", "canny-docs-10", "canny-probe-3" ] }, { "productId": "canny", "storyId": "agentic-nl-commands", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Canny ships an official MCP server enabling natural-language interaction via ChatGPT, Claude, or Cursor—users can ask questions about feedback, update statuses, link deals, and draft PRDs using plain language instead of UI/API calls. However, this is scoped to feedback/roadmap management rather than full product operation, and there's no independent/hands-on verification of the NL command experience quality. missing for 10: independent corroboration of MCP usability, breadth of natural-language commands beyond feedback workflows, evidence of reliability/limitations in practice.", "evidenceIds": [ "canny-docs-3", "canny-docs-4", "canny-docs-5", "canny-docs-9", "canny-docs-10", "canny-probe-3" ] }, { "productId": "canny", "storyId": "agentic-official-cli", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Evidence shows Canny has a REST API and an official MCP server, but nothing indicates an official CLI tool for AI-native workflows; no mention of any command-line interface anywhere in the docs.", "evidenceIds": [] }, { "productId": "canny", "storyId": "agentic-public-api", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Canny publishes a documented public API Reference (canny-docs-16) with a full developer docs site (canny-docs-15, canny-probe-1 llms.txt confirming developer docs), plus SDK/widget install docs and an official MCP server for LLM-driven interaction (canny-docs-3, canny-docs-9, canny-probe-3), enabling AI-native users to query and act on data programmatically. Missing for 10: an accessible OpenAPI/Swagger spec (canny-probe-2 shows all candidate OpenAPI endpoints 404) and independent hands-on corroboration of API usage.", "evidenceIds": [ "canny-docs-16", "canny-docs-15", "canny-probe-1", "canny-docs-3", "canny-docs-9", "canny-probe-3", "canny-probe-2" ] }, { "productId": "canny", "storyId": "agentic-scoped-keys", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Canny's docs mention a general API Reference and an MCP server that uses 'secure sign-in, no API keys' rather than any scoped or least-privilege credential system for agents; there is no evidence of granular API scopes, role-based tokens, or agent-specific permission controls.", "evidenceIds": [ "canny-docs-3", "canny-docs-16" ] }, { "productId": "canny", "storyId": "agentic-sdks", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Canny provides an official JavaScript SDK for identify calls, a documented REST API reference, and even an AI-friendly llms.txt file for developer docs, all of which support building against official SDKs. However, probes show no discoverable OpenAPI/swagger spec (404s across all candidate paths), and evidence only shows one language SDK (JS) rather than a broader official SDK ecosystem. missing for 10: OpenAPI/machine-readable API spec, multi-language SDKs, independent developer corroboration of SDK robustness.", "evidenceIds": [ "canny-docs-2", "canny-docs-16", "canny-probe-1", "canny-probe-2", "canny-docs-15" ] }, { "productId": "canny", "storyId": "agentic-webhooks", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack includes API reference, integrations, and MCP server docs but contains no mention of webhooks or event subscription mechanisms anywhere. This is an applicable axis for a feedback platform with an API, but no evidence supports it, so verdict is none.", "evidenceIds": [] }, { "productId": "canny", "storyId": "ai-declutter-feedback", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Canny's Autopilot and MCP features let AI surface themes, ARR impact, and top requests from raw feedback across sources (support, sales, reviews), and the MCP integration lets users query/act on this via ChatGPT/Claude. However, evidence is entirely vendor-authored marketing copy with no independent validation of how well the AI actually declutters/prioritizes feedback, no detail on the underlying scoring/impact methodology, and no hands-on or third-party corroboration. missing for 10: independent/hands-on evidence of AI decluttering quality, methodology detail behind 'ARR impact' scoring, third-party validation.", "evidenceIds": [ "canny-docs-4", "canny-docs-6", "canny-docs-7", "canny-docs-10", "canny-docs-11", "canny-probe-3" ] }, { "productId": "canny", "storyId": "ai-draft-task-from-feedback", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Canny's MCP lets an LLM query and act on feedback, including drafting PRDs and organizing/linking requests, which implies AI could turn feedback into structured tasks, but there's no explicit feature or workflow described that ingests a plain-language feedback description and outputs a structured task list. Missing for 10: a dedicated 'feedback-to-task' generation feature, hands-on example of the described workflow, and evidence of structured task output format.", "evidenceIds": [ "canny-docs-10", "canny-docs-5", "canny-docs-9", "canny-docs-4" ] }, { "productId": "canny", "storyId": "api-interactive-docs", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Canny has an API Reference page (canny-docs-16) but there is no evidence it includes runnable/interactive examples (e.g., try-it console, live code execution), and probes for a standard OpenAPI spec file all returned 404, suggesting no discoverable interactive spec.", "evidenceIds": [ "canny-docs-16", "canny-probe-2" ] }, { "productId": "canny", "storyId": "api-machine-spec", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Canny has an API Reference page but there is no evidence of a downloadable OpenAPI/Swagger spec; explicit probes for openapi.json, swagger.json, and similar well-known paths all returned 404.", "evidenceIds": [ "canny-docs-16", "canny-probe-2" ] }, { "productId": "canny", "storyId": "api-sandbox", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of a sandbox/staging environment or test mode separate from production data; all docs reference live API, widgets, and MCP integrations acting on real feedback data.", "evidenceIds": [] }, { "productId": "canny", "storyId": "api-versioning-policy", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Canny has an API Reference and install docs, but there is no evidence of API versioning or a documented deprecation policy; the OpenAPI spec probe returned 404s at all candidate paths, suggesting no machine-readable versioned spec either.", "evidenceIds": [ "canny-docs-16", "canny-probe-2" ] }, { "productId": "canny", "storyId": "approve-task-contract", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Canny's evidence covers feedback collection, roadmap status workflows, and syncing to task trackers (Jira, Linear, GitHub, etc.), but there is no documented feature for a PM to formally review/approve a task's scope before work starts — Canny hands off to the external tracker once linked, and no approval gate is described.", "evidenceIds": [] }, { "productId": "canny", "storyId": "automated-sla-workflows", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Canny's evidence covers feedback capture, integrations (Jira, Linear, GitHub, etc.), changelog, and an MCP server for querying/acting on feedback data, but there is no mention of automated workflow builders for SLAs, trial extensions, or discount offers triggered by feedback events. Missing for 10: any SLA automation, trial-extension logic, discount-offer triggers, or general conditional workflow/rules engine tied to feedback events.", "evidenceIds": [] }, { "productId": "canny", "storyId": "automated-status-updates", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Canny's changelog feature is described as a way to 'show customers you heard them' when shipping features (canny-docs-8), which implies some notification workflow, but the evidence never explicitly confirms an automated per-voter email/notification triggered specifically when a status changes on their requested feature — the closest matches are Discord alerts for team feedback (canny-intdir-1) and changelog publishing, not confirmed automatic customer-facing status-change alerts. Missing for 10: explicit documentation of automatic subscriber/voter notifications tied to individual status transitions, and any independent/hands-on confirmation of this workflow.", "evidenceIds": [ "canny-docs-8", "canny-intdir-1" ] }, { "productId": "canny", "storyId": "automation-bulk-operations", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Canny's docs show per-item actions (update status, link deals, sync to trackers) via its API and MCP server, but no evidence of any batch/bulk endpoint or MCP action that operates across many items at once — missing for 10: bulk update/merge API, batch MCP tool calls, any documented multi-item operation.", "evidenceIds": [] }, { "productId": "canny", "storyId": "automation-rules-engine", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows Canny automatically captures feedback from integrations and syncs statuses, but there is no documentation of user-defined rules/triggers that fire actions on specific events (e.g., an automation/workflow rule engine). Missing for 10: explicit rule-builder or trigger-condition-action documentation, evidence of customizable automation logic, examples of event-based automation beyond passive data capture.", "evidenceIds": [ "canny-docs-7", "canny-docs-11" ] }, { "productId": "canny", "storyId": "automation-scheduled-jobs", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Canny's evidence covers feedback capture, integrations, and an MCP server for querying/acting on feedback via LLM tools, but there is no mention of scheduling recurring jobs, cron-like automation, or persistent scheduled workflows. Autopilot automates capture but this is event-triggered, not schedule-based recurring automation.", "evidenceIds": [] }, { "productId": "canny", "storyId": "automation-versioned-workflows", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Canny is a customer feedback/roadmap management product, not an automation-building tool with versioned workflows; there is no concept of 'automations' to version, review, or roll back in this product category, making this a category error rather than a missing feature.", "evidenceIds": [] }, { "productId": "canny", "storyId": "branded-changelog-page", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Canny explicitly offers a Changelog product for announcing shipped features (canny-docs-8, canny-docs-14, canny-docs-26), confirming the axis applies, but none of the evidence describes custom-domain hosting or brand customization (logo, colors, CSS) for the changelog page. Missing for 10: explicit documentation of custom domain support, branding/theming options, and any independent confirmation of a fully white-labeled changelog experience.", "evidenceIds": [ "canny-docs-8", "canny-docs-14", "canny-docs-26" ] }, { "productId": "canny", "storyId": "capture-feedback-from-conversations", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Canny's Autopilot feature explicitly automates capturing feedback from sales calls (Gong, Fireflies, Fathom transcripts), support tickets (Intercom, support platforms), and chat (Slack, Discord), aggregating them into Canny as a unified feedback layer. This directly matches the story's capture-intake theme across multiple channel types with named integrations. Missing for 10: independent/hands-on verification of Autopilot's accuracy or reliability in practice, and clearer detail on chat conversation capture beyond Slack/Discord notifications.", "evidenceIds": [ "canny-docs-7", "canny-docs-11", "canny-intdir-2", "canny-intdir-3", "canny-docs-6" ] }, { "productId": "canny", "storyId": "centralized-feedback-repository", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Canny's core product is a centralized feedback repository: it captures requests directly from customers via Portal/widgets and automatically ingests feedback from Gong, Intercom, Slack, Fathom, Fireflies, and other tools via Autopilot, consolidating everything with statuses, syncing, and MCP-based query access for PMs to interrogate the whole feedback base. Missing for 10: explicit documentation of a search feature/UI within the repository, and independent/hands-on corroboration beyond vendor docs.", "evidenceIds": [ "canny-docs-7", "canny-docs-11", "canny-intdir-2", "canny-intdir-3", "canny-docs-4", "canny-docs-9", "canny-docs-17", "canny-docs-18" ] }, { "productId": "canny", "storyId": "changelog-email-subscriptions", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence confirms Canny has a Changelog feature for announcing shipped updates (canny-docs-8, canny-docs-14, canny-docs-26), but none of the evidence describes a customer-facing email subscription mechanism for new changelog entries. Missing for 10: any documentation of an email subscribe/opt-in feature tied to changelog publishing.", "evidenceIds": [ "canny-docs-8", "canny-docs-14", "canny-docs-26" ] }, { "productId": "canny", "storyId": "connect-support-tools", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Canny documents native integrations explicitly aimed at support, sales, and chat sources (Gong, Intercom, Slack, Fathom, Fireflies) via Autopilot, plus a broader integrations directory and MCP access from chat-based AI tools, directly matching the PM's need to connect feedback capture to existing tools. Missing for 10: independent/hands-on verification of these integrations working in practice and more detail on setup/configuration beyond marketing copy.", "evidenceIds": [ "canny-docs-7", "canny-docs-11", "canny-intdir-2", "canny-intdir-3", "canny-intdir-1", "canny-docs-22" ] }, { "productId": "canny", "storyId": "convert-feedback-to-task", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Canny lets PMs directly convert feedback into actionable work: integrations sync feedback to Jira, Linear, GitHub, ClickUp, and Asana as tasks/issues with status sync, and the MCP server allows updating statuses, linking deals, organizing requests, and even drafting PRDs from real requests directly from an LLM workflow. This covers the feedback-to-task pipeline end-to-end with both native integrations and AI-assisted workflows. Missing for 10: independent/hands-on corroboration beyond vendor docs, and more detail on native in-app task/project creation without third-party tool integration.", "evidenceIds": [ "canny-intdir-4", "canny-intdir-5", "canny-intdir-6", "canny-intdir-7", "canny-intdir-8", "canny-docs-5", "canny-docs-10" ] }, { "productId": "canny", "storyId": "customer-context-for-prioritization", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Canny lets PMs identify users/companies via SDK, attach custom fields, and view ARR impact and themes alongside feedback, plus MCP integration lets PMs query customer context (themes, ARR impact, roadmap) directly from LLM tools. Missing for 10: independent/hands-on validation of how customer context is surfaced in the actual UI (only docs/marketing evidence, no third-party corroboration).", "evidenceIds": [ "canny-docs-2", "canny-docs-4", "canny-docs-20", "canny-docs-23", "canny-docs-9", "canny-docs-10" ] }, { "productId": "canny", "storyId": "customize-widget-appearance", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Canny provides docs for embedding a web/mobile feedback widget (canny-docs-17, canny-docs-18) and its dev docs index mentions 'widget customization' (canny-probe-1), implying some branding controls, but no evidence explicitly confirms label text or color customization options. missing for 10: explicit documentation of label/text customization, explicit color/theme customization settings, and independent confirmation of brand-matching capability.", "evidenceIds": [ "canny-docs-17", "canny-docs-18", "canny-probe-1" ] }, { "productId": "canny", "storyId": "docs-editor-for-announcements", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence covers Canny's changelog feature, MCP integration, feedback capture and integrations, but nothing describes a rich text editor with custom components for authoring changelog/help content. Missing for 10: any mention of an editor, rich text formatting capabilities, custom components/blocks, or help-content authoring tools.", "evidenceIds": [] }, { "productId": "canny", "storyId": "embed-feedback-widget", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Canny documents a website widget install page (canny-docs-17) confirming embeddable widget support, but the evidence doesn't specify a single-script-tag, no-build-step method — docs also mention SDK install, SSO, and security/identify requests that suggest additional setup steps beyond a trivial snippet. missing for 10: actual snippet/code sample showing single script tag, confirmation that no build step or bundler is required, independent/hands-on verification of ease of embed.", "evidenceIds": [ "canny-docs-17", "canny-docs-2", "canny-docs-15" ] }, { "productId": "canny", "storyId": "embed-integrations-in-ui", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Canny's developer docs cover embedding Canny's own widget into a customer's app, its API/SDK, and native integrations that push feedback out to third-party tools (Jira, Linear, GitHub, etc.) or connect via MCP to external AI tools — but there is no evidence of a platform, app marketplace, or extension framework letting developers build custom integrations that run/appear directly inside Canny's own interface (dashboard, portal, or admin UI).", "evidenceIds": [] }, { "productId": "canny", "storyId": "embeddable-changelog-widget", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Canny explicitly offers a Changelog product for announcing shipped updates plus documented widget embed guides for web and mobile, directly matching the story. Missing for 10: independent/hands-on corroboration of the widget's ease-of-use and no evidence of changelog-specific widget customization details beyond general docs.", "evidenceIds": [ "canny-docs-8", "canny-docs-14", "canny-docs-17", "canny-docs-18", "canny-docs-26" ] }, { "productId": "canny", "storyId": "identify-users-in-feedback", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "Canny's JS SDK explicitly identifies users so feedback ties to their existing account (canny-docs-2), backed by dedicated SSO and secure identify-request docs (canny-docs-19, canny-docs-21/28) plus widget/mobile install docs (canny-docs-17,18). Missing for 10: independent/hands-on verification beyond vendor docs.", "evidenceIds": [ "canny-docs-2", "canny-docs-19", "canny-docs-21", "canny-docs-17", "canny-docs-18" ] }, { "productId": "canny", "storyId": "jump-to-item-by-shortname", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence describes a search/navigation feature for jumping directly to a feedback item, task, or initiative by typing its short name (e.g., a quick-open/command-palette shortcut). The evidence pack covers MCP integrations, widget install, changelog, and third-party sync tools, none of which address this navigation UX.", "evidenceIds": [] }, { "productId": "canny", "storyId": "link-feedback-to-customers", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Canny's JS SDK identifies users so feedback is tied to their existing account (canny-docs-2), and Install Companies docs support linking feedback to customer/company records (canny-docs-23). SSO and secure identify further ensure feedback is authenticated to real customers (canny-docs-19, canny-docs-21, canny-docs-27, canny-docs-28). Missing for 10: independent/hands-on corroboration beyond vendor docs, and explicit UI screenshots showing a feedback item's linked customer profile.", "evidenceIds": [ "canny-docs-2", "canny-docs-23", "canny-docs-19", "canny-docs-21" ] }, { "productId": "canny", "storyId": "link-feedback-to-roadmap-items", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Evidence shows Canny's MCP layer lets teams 'query your team's roadmap,' 'update statuses, link deals, organize requests,' and the changelog feature turns closed feedback into shipped announcements, implying some link between feedback and roadmap/release stages, but there is no explicit documentation of a native roadmap board where feedback items are directly attached to specific initiatives or releases. Missing for 10: dedicated docs on roadmap board structure, initiative/epic linking, and release-tagging of feedback items beyond generic issue-tracker sync (Jira/Linear/GitHub).", "evidenceIds": [ "canny-docs-4", "canny-docs-5", "canny-docs-8", "canny-intdir-4", "canny-intdir-5" ] }, { "productId": "canny", "storyId": "link-spec-to-delivery", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Canny lets teams sync feedback/roadmap items into delivery tools like Jira, Linear, GitHub, ClickUp and Asana (linking issues, syncing statuses), and its MCP integration explicitly supports drafting PRDs from real requests and building features with Claude, which maps to pulling an approved spec into implementation. However, this relies on third-party issue trackers rather than a native 'approved spec' handoff step, and there's no dedicated documentation of an approval gate or spec format being pulled into a dev workflow. missing for 10: a native concept of an 'approved' spec/status gate, first-party workflow docs showing the full approve-to-implement handoff, and independent/hands-on confirmation beyond vendor pages.", "evidenceIds": [ "canny-docs-10", "canny-intdir-4", "canny-intdir-5", "canny-intdir-6", "canny-intdir-7", "canny-intdir-8", "canny-docs-5" ] }, { "productId": "canny", "storyId": "link-to-feedback-site", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "Canny's docs explicitly instruct developers to add a link to the Canny feedback site in their app, plus provide widget embed options for web and mobile as alternatives/complements to a plain link. Missing for 10: independent/hands-on confirmation beyond first-party docs.", "evidenceIds": [ "canny-docs-1", "canny-docs-17", "canny-docs-18", "canny-docs-15" ] }, { "productId": "canny", "storyId": "one-click-integration-setup", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Canny documents a native integrations directory covering Jira, Linear, GitHub, ClickUp, Asana, Slack, Discord, Intercom, Gong, Fathom, Fireflies and others, described as easy native connections that sync statuses and capture feedback automatically, plus a low-friction MCP connection to ChatGPT/Claude/Cursor with 'no API keys, no engineering.' Missing for 10: independent hands-on confirmation of the actual click-count/setup friction and no evidence of a broader app marketplace beyond the listed integrations.", "evidenceIds": [ "canny-docs-7", "canny-docs-11", "canny-intdir-1", "canny-intdir-2", "canny-intdir-3", "canny-intdir-4", "canny-intdir-5", "canny-intdir-6", "canny-intdir-7", "canny-intdir-8", "canny-docs-3", "canny-docs-22" ] }, { "productId": "canny", "storyId": "openness-api-parity", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Canny publishes a public API reference and an MCP server that lets AI tools query and act on feedback data (statuses, replies, linking), suggesting meaningful API coverage, but no evidence establishes full parity between the API/MCP surface and everything available in the Canny UI (e.g., admin settings, roadmap customization, changelog publishing, integrations config). Missing for 10: explicit documentation or independent confirmation of complete UI-API parity, and an OpenAPI spec (probe found only 404s) that would demonstrate full endpoint coverage.", "evidenceIds": [ "canny-docs-16", "canny-docs-9", "canny-docs-5", "canny-probe-2", "canny-probe-3" ] }, { "productId": "canny", "storyId": "openness-full-export", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "While Canny provides an API reference and developer docs, there is no evidence of a bulk data export feature, open-format export (CSV/JSON dump of feedback, users, etc.), or any documented data-portability/account-closure workflow. The axis applies to a SaaS feedback platform, but no capability is evidenced.", "evidenceIds": [] }, { "productId": "canny", "storyId": "openness-open-license", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Canny is a closed, proprietary SaaS product with no evidence of any open-source license or public source code repository; all evidence concerns hosted feedback/MCP/integration features, not source availability.", "evidenceIds": [] }, { "productId": "canny", "storyId": "openness-self-host", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Canny is a hosted SaaS feedback platform with no evidence of a self-hostable/open-source core product; all docs describe cloud install/SDK/widget/MCP integrations, not self-hosting options.", "evidenceIds": [] }, { "productId": "canny", "storyId": "prioritization-scoring-fields", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Canny's Custom Fields feature (canny-docs-20) would let a PM add fields such as value, risk, impact, confidence, or effort to ideas, but the evidence pack contains no documentation of a purpose-built scoring/prioritization framework (e.g., RICE/ICE formulas, weighted score calculation, or a dedicated prioritization view). Missing for 10: evidence of built-in scoring formulas or automated priority-score calculation, evidence of a dedicated prioritization board/view using these fields.", "evidenceIds": [ "canny-docs-20" ] }, { "productId": "canny", "storyId": "privacy-data-residency", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence in the pack mentions data residency, regional storage options, or any control over where customer data is hosted; Canny's docs focus on install, SDKs, MCP, and integrations only.", "evidenceIds": [] }, { "productId": "canny", "storyId": "privacy-no-training", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack addresses AI training data opt-out, data usage policies for model training, or privacy controls preventing Canny from using customer data to train AI models; the pack only covers MCP feedback tooling and integrations. missing for 10: any privacy policy, ToS clause, or admin setting about excluding data from AI/ML training.", "evidenceIds": [] }, { "productId": "canny", "storyId": "privacy-retention-controls", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence in the pack addresses data retention policies, deletion controls, or privacy/data-lifecycle management for user or feedback data; the docs cover SSO, install, widgets, MCP, and integrations but nothing about retention/deletion mechanisms.", "evidenceIds": [] }, { "productId": "canny", "storyId": "privacy-telemetry-optout", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence pack items mention telemetry, usage tracking, analytics opt-out, or any privacy/data-collection controls for AI-native or MCP usage; the documentation focuses on feedback collection, integrations, and MCP feature capabilities rather than telemetry controls. missing for 10: any mention of telemetry/usage-data collection, an opt-out mechanism, or privacy settings related to AI tool usage.", "evidenceIds": [] }, { "productId": "canny", "storyId": "product-hierarchy-management", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack contains no mention of hierarchical organization of feedback into features, components, or initiatives (e.g., no reference to Canny's category/board structure or initiative grouping) — only integrations, MCP, changelog, and SDK install docs are present. Missing for 10: any docs on categories, tagging hierarchy, or initiative/epic grouping features.", "evidenceIds": [] }, { "productId": "canny", "storyId": "public-feedback-portal", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Canny's core product is a public feedback portal embeddable via widget (web/mobile) where customers submit and vote on ideas, with SSO/identify integration and a changelog to close the loop; this is well documented across install and widget docs. Missing for 10: no independent/hands-on third-party review confirming the voting/portal UX in practice, and no explicit customer-facing voting mechanic quote (only 'submit feedback on your Portal').", "evidenceIds": [ "canny-docs-17", "canny-docs-18", "canny-docs-1", "canny-docs-7", "canny-docs-8", "canny-docs-19" ] }, { "productId": "canny", "storyId": "publish-changelog", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Canny explicitly offers a Changelog product ('Announce what you shipped, show customers you heard them, and turn closed feedback into retention and upgrades') as a dedicated feature area, directly matching the story of closing the loop with customers on shipped features. Missing for 10: no evidence of workflow automation linking closed feedback items to changelog entries, no screenshots/hands-on detail on publishing flow, and no independent/user corroboration beyond marketing copy.", "evidenceIds": [ "canny-docs-8", "canny-docs-14", "canny-docs-26" ] }, { "productId": "canny", "storyId": "query-feedback-themes-via-ai", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Canny's MCP server explicitly lets users ask their LLM about customer feedback themes and ARR/revenue impact ('Dig into top themes, ARR impact across ideas'), connecting via ChatGPT/Claude/Cursor without engineering work. This directly matches the story's ask-AI-about-feedback-and-revenue-impact use case. Missing for 10: independent/hands-on verification beyond vendor docs, and concrete examples of the AI answering real revenue-impact queries.", "evidenceIds": [ "canny-docs-3", "canny-docs-4", "canny-docs-9", "canny-docs-10", "canny-probe-3" ] }, { "productId": "canny", "storyId": "quick-portal-setup", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Canny's core product is a hosted feedback portal, and docs describe simply linking to 'your Canny feedback site' (canny-docs-1) plus a changelog tool (canny-docs-8/14) that require no code to use. However, deeper setup steps described (SDK install, widget embedding, SSO, custom fields) are aimed at developers, and there's no explicit documentation walking a non-technical PM through portal creation without engineering. Missing for 10: explicit no-code onboarding wizard documentation, independent PM testimonials confirming self-serve setup without engineering.", "evidenceIds": [ "canny-docs-1", "canny-docs-8", "canny-docs-14", "canny-docs-17" ] }, { "productId": "canny", "storyId": "reply-to-customers-inline", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Evidence confirms replying to customers is possible via the MCP integration (\"reply to customers\" listed as an action from LLM tools), implying the underlying feedback record supports replies, but there is no direct documentation of the native in-app reply-to-customer workflow within the feedback record itself. missing for 10: direct product docs/screenshots of replying to a commenter within the Canny feedback record UI, evidence of email/notification triggered to the customer, and independent/hands-on confirmation of this closing-the-loop workflow.", "evidenceIds": [ "canny-docs-5", "canny-docs-9" ] }, { "productId": "canny", "storyId": "reschedule-initiatives", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence covers feedback capture, integrations, MCP/AI tooling, and changelog features but nothing about roadmap/release planning, initiative scheduling, or moving items between releases. Missing for 10: any mention of a roadmap tool, release/version grouping, or drag-and-drop scheduling of initiatives.", "evidenceIds": [] }, { "productId": "canny", "storyId": "surface-related-feature-insights", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Canny's Autopilot automatically captures feedback from calls, support, and sales tools, and its MCP server lets an LLM be asked about 'top themes' and context behind requests, suggesting some AI-driven insight surfacing (canny-docs-4, canny-docs-11, canny-intdir-2/3). However, there is no evidence of the AI proactively detecting insights and automatically linking them to existing/related feature ideas within Canny itself — the linking features shown (Jira, Linear, GitHub, ClickUp, Asana) are manual issue-tracker syncs, not AI-detected relations between feedback and ideas. Missing for 10: evidence of automatic duplicate/related-idea detection, proof of unsupervised insight-to-idea linking (vs. on-demand LLM queries), and independent validation of this capability.", "evidenceIds": [ "canny-docs-4", "canny-docs-11", "canny-intdir-2", "canny-intdir-3", "canny-docs-10" ] }, { "productId": "canny", "storyId": "targeted-release-notes", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Canny's changelog feature (canny-docs-8, canny-docs-14) covers publishing release notes, and separate docs mention custom fields and company identification (canny-docs-20, canny-docs-23), but no evidence ties these together to show audience segmentation of changelog/release announcements by user attributes for targeted delivery.", "evidenceIds": [ "canny-docs-8", "canny-docs-14", "canny-docs-20", "canny-docs-23" ] }, { "productId": "canny", "storyId": "track-task-progress", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Canny syncs statuses with tools like Jira, Linear, GitHub, ClickUp, and Asana, so a PM could see a high-level status label update via those integrations, but there is no evidence of a dedicated real-time view showing why a task is blocked or a detailed 'last update' timeline. missing for 10: explicit blocked-reason field, real-time update timestamps/activity feed, dedicated in-progress task dashboard.", "evidenceIds": [ "canny-intdir-4", "canny-intdir-5", "canny-intdir-6", "canny-intdir-7", "canny-intdir-8" ] }, { "productId": "canny", "storyId": "usage-analytics-adoption", "verdict": "na", "quality": 0, "confidence": "medium", "rationale": "Canny is a feedback/roadmap and changelog management tool; it captures and organizes customer feedback but does not measure actual in-product usage or feature adoption analytics. Asking for 'usage analytics' to gauge product adoption is a category mismatch for a feedback-management platform, not a gap in Canny's own feedback tooling.", "evidenceIds": [] }, { "productId": "featurebase", "storyId": "agentic-agent-docs", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "A direct probe confirms Featurebase serves a valid llms.txt at https://developers.featurebase.app/llms.txt returning HTTP 200 with structured docs links, which is exactly what an AI agent would need to consume agent-oriented documentation. missing for 10: no independent third-party corroboration of agent usage beyond the probe itself.", "evidenceIds": [ "featurebase-probe-1" ] }, { "productId": "featurebase", "storyId": "agentic-ai-insights", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Featurebase explicitly claims AI helps 'declutter & find the most impactful opportunities' from feedback data, and separately offers AI-driven search answers and an AI support agent (Fibi), suggesting some AI-generated insight capability inside the product. However, evidence is a single marketing line with no detail on what insights look like, how they're surfaced, or independent/hands-on corroboration. Missing for 10: detailed documentation of the insights feature (e.g., trend detection, sentiment analysis, prioritization scoring), screenshots/demos, and independent user validation.", "evidenceIds": [ "featurebase-docs-7", "featurebase-docs-10", "featurebase-docs-14" ] }, { "productId": "featurebase", "storyId": "agentic-autonomous-automation", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Featurebase offers built-in 'workflows' for SLAs, trial extensions, and automatic notifications, plus webhooks for backend reactions and an AI agent (Fibi) that can auto-resolve support issues — these could run autonomously in the background. However, there is no explicit documentation of a user-configurable automation/workflow builder exposed as an agentic API, nor evidence of arbitrary background automations triggered by AI-native users beyond these built-in features. missing for 10: documentation of a general-purpose automation/workflow API for AI-native users, evidence of user-defined autonomous triggers beyond fixed built-in workflows, and independent confirmation of these features working end-to-end.", "evidenceIds": [ "featurebase-docs-3", "featurebase-docs-10", "featurebase-docs-12", "featurebase-docs-1" ] }, { "productId": "featurebase", "storyId": "agentic-builtin-assistant", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Featurebase advertises 'Fibi AI agent' that can automatically resolve customer issues and provide instant AI answers, which constitutes a built-in AI assistant that can be delegated support tasks. However, evidence is limited to marketing taglines with no detail on task delegation scope, configuration, or hands-on validation. Missing for 10: technical docs on delegating specific tasks to Fibi, independent/hands-on verification of its autonomy, and detail on scope of tasks it can handle beyond support tickets.", "evidenceIds": [ "featurebase-docs-7", "featurebase-docs-10", "featurebase-docs-14" ] }, { "productId": "featurebase", "storyId": "agentic-headless", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Featurebase exposes an HTTP API reference, a JS SDK, and webhooks that could in principle be scripted from CI/automation pipelines, but there is no explicit documentation, CLI, or example of running Featurebase headlessly in CI, and the OpenAPI spec itself is not discoverable at expected endpoints. Missing for 10: explicit CI/headless usage guide, discoverable OpenAPI/machine-readable spec, and a CLI or automation-first workflow example.", "evidenceIds": [ "featurebase-docs-1", "featurebase-docs-2", "featurebase-probe-1", "featurebase-probe-2" ] }, { "productId": "featurebase", "storyId": "agentic-mcp-client", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence Featurebase supports MCP server integration as a client to consume external tools; evidence only covers webhooks, integrations, changelog, AI answers, and Fibi agent features, none of which mention MCP.", "evidenceIds": [] }, { "productId": "featurebase", "storyId": "agentic-mcp-server", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Featurebase is a feedback/changelog SaaS platform (not an agent), so publishing an official MCP server is a plausible axis, but no evidence mentions an MCP server anywhere—only webhooks, API/SDK reference, and integrations are documented.", "evidenceIds": [ "featurebase-docs-1", "featurebase-docs-2", "featurebase-docs-4", "featurebase-probe-1", "featurebase-probe-2" ] }, { "productId": "featurebase", "storyId": "agentic-nl-commands", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows Fibi AI answering customer support queries and AI-assisted decluttering of feedback, but nothing indicates an admin/user can operate Featurebase itself (create boards, manage roadmaps, configure settings) via natural-language commands. Missing for 10: any NL command interface, chat-driven admin actions, or agentic control surface for operating the product.", "evidenceIds": [ "featurebase-docs-7", "featurebase-docs-10", "featurebase-docs-14" ] }, { "productId": "featurebase", "storyId": "agentic-official-cli", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows an API/SDK reference and a typed JS client SDK, but no mention anywhere of an official CLI tool for AI-native workflows.", "evidenceIds": [ "featurebase-docs-2", "featurebase-probe-1" ] }, { "productId": "featurebase", "storyId": "agentic-public-api", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Featurebase explicitly documents a full HTTP and SDK API reference plus webhooks for reacting to feedback/status changes, and its developer docs site is crawlable via llms.txt listing SDK guides, indicating a genuine documented public API surface for programmatic/AI-driven use. Missing for 10: a discoverable machine-readable OpenAPI/Swagger spec (probe returned 404s at standard paths) and independent third-party confirmation of API completeness/reliability.", "evidenceIds": [ "featurebase-docs-1", "featurebase-docs-2", "featurebase-probe-1", "featurebase-probe-2" ] }, { "productId": "featurebase", "storyId": "agentic-scoped-keys", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows Featurebase has an API/SDK reference and webhooks, but nothing about scoped or least-privilege API credentials, API key permission scoping, or agent-specific credential issuance. No mention of granular token scopes, roles, or restricted keys anywhere in the docs.", "evidenceIds": [ "featurebase-docs-2", "featurebase-probe-1", "featurebase-probe-2" ] }, { "productId": "featurebase", "storyId": "agentic-sdks", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Evidence shows a documented 'featurebase-js' typed client SDK for the Messenger widget and a generic API reference tab, indicating some official SDK exists, but the OpenAPI spec 404s and there's no broader SDK ecosystem (e.g., for other languages) or independent corroboration. missing for 10: multi-language SDKs, working OpenAPI/spec discoverability, independent developer confirmation of SDK usage.", "evidenceIds": [ "featurebase-probe-1", "featurebase-docs-2", "featurebase-probe-2" ] }, { "productId": "featurebase", "storyId": "agentic-webhooks", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Docs explicitly mention webhooks that let backend systems react to feedback, conversation activity, and status changes, confirming the capability exists, but there's no evidence of webhook payload schema, event catalog, signing/verification, or setup UI details. missing for 10: documented event types list, payload examples, subscription management UI/API, security/signing verification, independent confirmation of reliability.", "evidenceIds": [ "featurebase-docs-1", "featurebase-docs-2" ] }, { "productId": "featurebase", "storyId": "ai-declutter-feedback", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Featurebase explicitly markets an AI feature to 'declutter & find the most impactful opportunities' from feedback, directly matching the story, but only a single marketing tagline exists with no supporting documentation, screenshots, or independent corroboration of how it works. missing for 10: detailed docs/feature walkthrough on the AI decluttering feature, independent or hands-on verification, and technical specifics (e.g., what signals it uses to rank impact).", "evidenceIds": [ "featurebase-docs-7" ] }, { "productId": "featurebase", "storyId": "ai-draft-task-from-feedback", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Featurebase advertises AI features like decluttering feedback (featurebase-docs-7) and an AI agent for resolving support issues (featurebase-docs-10), but none of the evidence describes AI converting a plain-language feedback description into a structured set of proposed tasks. Missing for 10: any documentation of AI generating structured task/action breakdowns from feedback text, UI screenshots or workflow examples of this specific capability.", "evidenceIds": [ "featurebase-docs-7", "featurebase-docs-10", "featurebase-docs-14" ] }, { "productId": "featurebase", "storyId": "api-interactive-docs", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence confirms an 'API Reference' tab with HTTP/SDK docs exists, but nothing indicates it is interactive or runnable (no try-it console, no live examples), and probes for a standard OpenAPI/Swagger spec (which typically powers such interactive references) returned 404 at all checked paths.", "evidenceIds": [ "featurebase-docs-2", "featurebase-probe-2" ] }, { "productId": "featurebase", "storyId": "api-machine-spec", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Docs reference an 'API Reference' tab with HTTP/SDK docs, but there is no evidence of a downloadable OpenAPI/Swagger spec — probes for openapi.json, swagger.json, and well-known variants all returned 404, and llms.txt only lists guide pages, not a machine-readable spec.", "evidenceIds": [ "featurebase-docs-2", "featurebase-probe-1", "featurebase-probe-2" ] }, { "productId": "featurebase", "storyId": "api-sandbox", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of a sandbox/test environment, staging API keys, or test-mode toggle to isolate testing from production data; evidence only covers general product features and API docs discovery.", "evidenceIds": [] }, { "productId": "featurebase", "storyId": "api-versioning-policy", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Evidence shows an API reference and SDK exist, but there is no mention of API versioning scheme or any documented deprecation policy anywhere in the pack, and the OpenAPI probe returned 404s.", "evidenceIds": [ "featurebase-docs-2", "featurebase-probe-2" ] }, { "productId": "featurebase", "storyId": "approve-task-contract", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Featurebase's evidence covers feedback collection, roadmapping, changelog, integrations, webhooks, and support inbox, but nothing describes a workflow where a PM reviews or approves the scope of a task before work begins. Missing for 10: any task/scope definition step, an approval gate or sign-off mechanism, or documentation of a pre-work review process.", "evidenceIds": [] }, { "productId": "featurebase", "storyId": "automated-sla-workflows", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Featurebase's own marketing explicitly promises workflow automation for SLAs, trial extensions, and discount offers (featurebase-docs-3), backed by webhook support that reacts to feedback/status events (featurebase-docs-1). However, there is no documentation of the actual workflow builder UI, trigger/condition/action configuration, or independent/hands-on confirmation that these automations work as advertised. Missing for 10: detailed workflow-builder documentation, example configurations, and independent or hands-on verification of SLA/trial/discount automation in practice.", "evidenceIds": [ "featurebase-docs-3", "featurebase-docs-1" ] }, { "productId": "featurebase", "storyId": "automated-status-updates", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Featurebase explicitly documents automatic notification to users when their requested feature/status ships ('automatically notifies all relevant users when their requests are shipped. No more manual follow-ups'), directly matching the story, supplemented by webhooks reacting to status changes for further automation. Missing for 10: independent/hands-on confirmation of the notification triggering correctly and detail on configuring which status changes trigger alerts.", "evidenceIds": [ "featurebase-docs-12", "featurebase-docs-1" ] }, { "productId": "featurebase", "storyId": "automation-bulk-operations", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows Featurebase has an API/SDK and webhooks but no documentation or reference to any bulk/batch operation endpoints for updating, tagging, or managing many items at once. The openapi probe even failed to resolve a schema, so no detail on bulk operation support is available.", "evidenceIds": [ "featurebase-docs-2", "featurebase-probe-1", "featurebase-probe-2" ] }, { "productId": "featurebase", "storyId": "automation-rules-engine", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Featurebase docs mention webhooks that react instantly to feedback/status events and a 'workflows' feature for SLAs, auto-extending trials, and offering discounts, indicating rule-based automation tied to events. However, there is no detailed documentation of a rule-builder UI, condition/trigger types, or examples showing how a user defines custom automation logic beyond the marketing blurb. Missing for 10: dedicated workflow/rule-builder documentation, concrete trigger-condition-action examples, independent or hands-on verification of the automation engine's flexibility.", "evidenceIds": [ "featurebase-docs-1", "featurebase-docs-3", "featurebase-docs-12" ] }, { "productId": "featurebase", "storyId": "automation-scheduled-jobs", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows webhooks (reactive, event-driven), workflows for SLAs/trials, and an API/SDK, but nothing about scheduling recurring jobs or cron-like automation. No mention of scheduled triggers, recurring tasks, or job scheduling capabilities anywhere in the docs pack.", "evidenceIds": [ "featurebase-docs-1", "featurebase-docs-3" ] }, { "productId": "featurebase", "storyId": "automation-versioned-workflows", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Featurebase offers basic workflow automation (SLAs, trial extensions) but no evidence of version history, review process, or rollback capability for automations/workflows.", "evidenceIds": [] }, { "productId": "featurebase", "storyId": "branded-changelog-page", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "Docs explicitly state changelogs can be served from a standalone page with a custom domain and fully branded, plus email subscription support, directly matching the story. Missing for 10: independent/hands-on verification beyond vendor docs.", "evidenceIds": [ "featurebase-docs-9" ] }, { "productId": "featurebase", "storyId": "capture-feedback-from-conversations", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Featurebase's unified inbox and integrations support capturing feedback from in-app, Slack, Discord, and email conversations, and webhooks can react to new feedback/conversation activity, which covers chat and support-ticket-like channels. However, there is no evidence of any sales-call integration (e.g., call transcription tools like Gong) or automatic extraction of feedback from sales conversations specifically. Missing for 10: sales-call capture integration, evidence of automatic parsing/extraction of feedback content from tickets/calls rather than just channel presence.", "evidenceIds": [ "featurebase-docs-11", "featurebase-docs-4", "featurebase-docs-1", "featurebase-docs-15" ] }, { "productId": "featurebase", "storyId": "centralized-feedback-repository", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Featurebase explicitly markets a feedback portal that centralizes insights from customers and colleagues into a single searchable source of truth, with voting and public roadmap features, plus integrations to pull in requests from multiple channels. Missing for 10: independent/hands-on corroboration of search functionality quality and no explicit detail on internal colleague-only submission workflows.", "evidenceIds": [ "featurebase-docs-15", "featurebase-docs-11", "featurebase-docs-4" ] }, { "productId": "featurebase", "storyId": "changelog-email-subscriptions", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Featurebase explicitly documents a standalone changelog page where 'Subscribe users to email updates' is offered, directly matching the story, and also supports segmenting release notes by user attributes for targeted notifications. missing for 10: independent/hands-on confirmation of the subscription flow and details on subscription management (opt-out, frequency).", "evidenceIds": [ "featurebase-docs-9", "featurebase-docs-6", "featurebase-docs-12" ] }, { "productId": "featurebase", "storyId": "connect-support-tools", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Featurebase advertises multi-channel support inboxes (in-app, Slack, Discord, Email) and generic 'connect with your favorite tools' integrations plus webhooks/workflows for backend automation, which supports connecting feedback to support and chat tools. However, evidence never names specific sales-tool integrations (e.g., CRM/Salesforce/HubSpot) or provides a concrete integrations list/marketplace beyond vague marketing language, and there's no independent confirmation of integration depth. missing for 10: named sales-tool integrations, a documented integrations directory/marketplace, and third-party verification of real-world connections.", "evidenceIds": [ "featurebase-docs-4", "featurebase-docs-11", "featurebase-docs-1", "featurebase-docs-3" ] }, { "productId": "featurebase", "storyId": "convert-feedback-to-task", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Featurebase supports centralizing feedback into a roadmap/prioritization view (docs-15), general third-party integrations (docs-4), and webhooks for backend automation (docs-1), which together imply feedback can be routed into external task/project tools, but there is no explicit documentation of a native 'convert feedback to task' action or named integration (e.g., Jira/Linear/Asana) confirming this workflow. missing for 10: explicit feature/documentation showing one-click conversion of a feedback item into a task/issue in a project-management tool, named PM tool integrations, and independent confirmation of this workflow in practice.", "evidenceIds": [ "featurebase-docs-15", "featurebase-docs-4", "featurebase-docs-1" ] }, { "productId": "featurebase", "storyId": "customer-context-for-prioritization", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Featurebase centralizes feedback with a portal, integrations, and user-attribute segmentation, and uses AI to surface 'impactful opportunities,' which loosely supports contextualizing feedback for prioritization. However, there is no explicit evidence of surfacing customer-level context (e.g., account value, plan, company data) alongside individual feedback items to aid prioritization decisions. Missing for 10: direct documentation of customer/account context displayed next to feedback items, and any prioritization scoring tied to customer attributes.", "evidenceIds": [ "featurebase-docs-6", "featurebase-docs-15", "featurebase-docs-7", "featurebase-docs-4" ] }, { "productId": "featurebase", "storyId": "customize-widget-appearance", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack mentions embeddable widgets, custom domains, and branding for changelogs, but there is no mention of customizing the feedback widget's label text or color scheme specifically. Missing for 10: any documentation of widget label/text customization, color/theme customization options for the feedback capture widget.", "evidenceIds": [] }, { "productId": "featurebase", "storyId": "docs-editor-for-announcements", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Docs explicitly describe a 'Notion-like editor experience with tons of formatting options and custom components' for changelogs/help docs, directly matching the story. missing for 10: independent/hands-on corroboration of the editor, and no detail on which specific custom components are available.", "evidenceIds": [ "featurebase-docs-5", "featurebase-docs-9", "featurebase-docs-13" ] }, { "productId": "featurebase", "storyId": "embed-feedback-widget", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Featurebase advertises embeddable widgets (changelog and in-app feedback widgets) and a typed JS SDK, implying some form of client-side embed capability, but no evidence explicitly confirms a single script-tag, no-build-step integration for the feedback widget specifically. missing for 10: explicit script-tag embed instructions, confirmation of zero build step, and hands-on/independent verification of the feedback widget embed process.", "evidenceIds": [ "featurebase-docs-8", "featurebase-docs-15", "featurebase-probe-1" ] }, { "productId": "featurebase", "storyId": "embed-integrations-in-ui", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows webhooks, an API/SDK, and integrations connecting Featurebase to external tools, plus embeddable widgets for putting Featurebase content into a customer's own product — but nothing about a plugin/app framework that lets developers build custom functionality that runs inside Featurebase's own interface. Missing for 10: any documented app/plugin marketplace, custom UI extension points, or SDK for embedding third-party functionality within Featurebase's own dashboard.", "evidenceIds": [ "featurebase-docs-1", "featurebase-docs-2", "featurebase-docs-4", "featurebase-docs-8" ] }, { "productId": "featurebase", "storyId": "embeddable-changelog-widget", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Featurebase explicitly advertises embeddable changelog widgets to showcase updates directly in-product and on websites, plus a standalone branded changelog page with email subscriptions. Missing for 10: independent/hands-on verification of the widget embed process and customization depth.", "evidenceIds": [ "featurebase-docs-8", "featurebase-docs-9" ] }, { "productId": "featurebase", "storyId": "identify-users-in-feedback", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "None of the evidence describes identifying logged-in users (e.g., SSO/JWT identity verification tying feedback to an existing account); only generic SDK, webhook, and integration mentions are present with no user-identification specifics.", "evidenceIds": [] }, { "productId": "featurebase", "storyId": "jump-to-item-by-shortname", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "This story describes a developer navigation/shortcut feature (jumping to items by short name/ID), which is an IDE or project-management UI convenience, not applicable to Featurebase's feedback/changelog management product category.", "evidenceIds": [] }, { "productId": "featurebase", "storyId": "link-feedback-to-customers", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "The evidence pack does not directly describe any capability for tagging or linking feedback submissions to individual customer identities/profiles (e.g., CRM records, customer attributes). Only tangential mentions of notifying 'relevant users' or workflow automation exist, not the specific submitter-linking capability.", "evidenceIds": [ "featurebase-docs-12", "featurebase-docs-15" ] }, { "productId": "featurebase", "storyId": "link-feedback-to-roadmap-items", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Featurebase's public roadmap and feedback portal (featurebase-docs-15) let users vote on ideas and see a roadmap, and shipped requests automatically notify relevant users (featurebase-docs-12), implying feedback items are tied to roadmap statuses/releases. However, there's no explicit documentation of linking feedback to 'initiatives' or granular release notes tied to specific feedback items. Missing for 10: explicit initiative/epic linking feature, documentation of feedback-to-release mapping UI, and independent confirmation of this workflow.", "evidenceIds": [ "featurebase-docs-15", "featurebase-docs-12" ] }, { "productId": "featurebase", "storyId": "link-spec-to-delivery", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Featurebase advertises webhooks and generic third-party integrations that could relay approved feedback/specs into other systems, but there is no concrete evidence of a specific integration (e.g., Jira/Linear/GitHub) that pulls an approved spec into a delivery workflow for implementation. Missing for 10: documented integration with issue trackers/dev tools, evidence of spec-to-ticket sync, and any developer-facing workflow showing implementation kickoff from an approved post.", "evidenceIds": [ "featurebase-docs-1", "featurebase-docs-3", "featurebase-docs-4" ] }, { "productId": "featurebase", "storyId": "link-to-feedback-site", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Featurebase's product centers on a hosted feedback portal with embeddable in-app widgets (featurebase-docs-15), which implies developers can link/embed the portal in their product, but there's no explicit documentation snippet showing a code sample, widget script, or direct 'add this link' instructions. Missing for 10: explicit widget/embed code documentation, confirmation of a direct portal URL/link feature, and independent evidence of ease of integration.", "evidenceIds": [ "featurebase-docs-15" ] }, { "productId": "featurebase", "storyId": "one-click-integration-setup", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Marketing copy explicitly claims one-click integrations with popular tools ('Connect Featurebase with your favorite tools in just a few clicks'), but there's no evidence pack listing which specific third-party tools (Slack, Jira, Intercom, etc.) are supported or showing the actual connection flow/UI. missing for 10: list of specific supported integrations, screenshots/docs of the setup flow, independent confirmation of ease-of-use.", "evidenceIds": [ "featurebase-docs-4", "featurebase-docs-11" ] }, { "productId": "featurebase", "storyId": "openness-api-parity", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Featurebase documents an API reference, SDK, and webhooks (developers.featurebase.app), showing a real API surface exists, but there is no evidence claiming or demonstrating full feature parity between the UI and API — no OpenAPI spec was found (404s on all candidate paths), and admin/workflow/changelog features are described only as UI capabilities. Missing for 10: explicit parity documentation, published OpenAPI spec, and evidence that UI-only features (workflows, changelog widgets, integrations) are also exposed via API.", "evidenceIds": [ "featurebase-docs-2", "featurebase-probe-1", "featurebase-probe-2", "featurebase-docs-1" ] }, { "productId": "featurebase", "storyId": "openness-full-export", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence of a bulk data-export feature, open-format export, or data-portability tooling; only webhooks, an API reference, and unrelated feature docs are provided. missing for 10: documented full data export tool, open format (CSV/JSON) export, account data portability guarantee.", "evidenceIds": [] }, { "productId": "featurebase", "storyId": "openness-open-license", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence Featurebase's source code is available under any open license; it is a closed SaaS product with only API docs, SDKs, and integrations mentioned.", "evidenceIds": [] }, { "productId": "featurebase", "storyId": "openness-self-host", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Featurebase is a hosted SaaS feedback/support platform with no evidence of a self-hosted deployment option; self-hosting is not a claimed or implied capability of this product category as evidenced here, making this a category mismatch rather than a missing feature.", "evidenceIds": [] }, { "productId": "featurebase", "storyId": "prioritization-scoring-fields", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence of custom scoring fields (value, risk, impact, confidence, effort) or a prioritization scoring framework anywhere in the docs; the pack only covers feedback collection, changelogs, support inbox, and integrations.", "evidenceIds": [] }, { "productId": "featurebase", "storyId": "privacy-data-residency", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence pack item mentions data residency, region selection, or data storage location options; the docs cover integrations, changelogs, AI search, and support features but say nothing about where data is hosted or a choice of region.", "evidenceIds": [] }, { "productId": "featurebase", "storyId": "privacy-no-training", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence pack item mentions any AI training data opt-out, privacy policy on model training, or data usage controls for AI features like Fibi or AI search; the docs focus on product features (webhooks, integrations, changelogs) with no privacy-posture statements.", "evidenceIds": [] }, { "productId": "featurebase", "storyId": "privacy-retention-controls", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of data retention controls, deletion APIs, or privacy/data-lifecycle policies anywhere in the docs; evidence only covers feedback, changelog, and support features unrelated to data retention/deletion.", "evidenceIds": [] }, { "productId": "featurebase", "storyId": "privacy-telemetry-optout", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence in the pack mentions telemetry, usage tracking, or an opt-out mechanism for Featurebase itself; all items describe feedback/roadmap/support features unrelated to privacy controls.", "evidenceIds": [] }, { "productId": "featurebase", "storyId": "product-hierarchy-management", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack covers feedback portals, roadmaps, changelogs, integrations, and AI support tools, but none of it mentions organizing requests into a hierarchy of features, components, and initiatives. Missing for 10: any mention of parent-child feature grouping, initiative-level rollups, or component hierarchies.", "evidenceIds": [] }, { "productId": "featurebase", "storyId": "public-feedback-portal", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "Featurebase explicitly offers a public feedback portal where users vote on each other's ideas and view a public roadmap, directly matching the story. Missing for 10: independent/hands-on third-party corroboration beyond vendor docs.", "evidenceIds": [ "featurebase-docs-15" ] }, { "productId": "featurebase", "storyId": "publish-changelog", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Featurebase explicitly supports publishing changelogs with a Notion-like editor, custom domain/branding, embeddable widgets, email subscriptions, audience segmentation, and automatic notifications to users when their requests ship — directly closing the loop with customers. missing for 10: independent/hands-on corroboration beyond vendor marketing pages, and no detailed workflow documentation on drafting/approval process for changelog publishing.", "evidenceIds": [ "featurebase-docs-5", "featurebase-docs-6", "featurebase-docs-8", "featurebase-docs-9", "featurebase-docs-12" ] }, { "productId": "featurebase", "storyId": "query-feedback-themes-via-ai", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Featurebase's AI features documented are Fibi (support ticket resolution) and AI-powered search for customers, plus general 'declutter with AI' framing—none evidence a conversational assistant that analyzes feedback themes and ties them to revenue impact.", "evidenceIds": [ "featurebase-docs-7", "featurebase-docs-10", "featurebase-docs-14" ] }, { "productId": "featurebase", "storyId": "quick-portal-setup", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Featurebase is explicitly a no-code feedback portal product ('feedback portal, in-app widgets... let users vote on ideas & show a public roadmap') with custom domain and branding options for changelogs, suggesting a self-serve setup without engineering. However, there is no explicit walkthrough or evidence describing the actual portal setup flow, time-to-launch, or confirmation that no code/dev work is required for the core portal (only for changelog/domain features). missing for 10: explicit onboarding/setup documentation for the feedback portal itself, confirmation of no-code setup, independent user testimony on ease of setup without engineering.", "evidenceIds": [ "featurebase-docs-15", "featurebase-docs-9", "featurebase-docs-4" ] }, { "productId": "featurebase", "storyId": "reply-to-customers-inline", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Featurebase's 'single powerful inbox' (docs-11) and automatic shipped-notifications (docs-12) imply admins can communicate with customers from a centralized feedback/support view, which aligns with the story's intent, but no evidence explicitly shows replying inline within a feedback record's comment thread to close the loop. missing for 10: explicit documentation or screenshot of replying to a specific feedback post/comment, confirmation that replies notify the customer directly, and any independent/hands-on corroboration.", "evidenceIds": [ "featurebase-docs-11", "featurebase-docs-12" ] }, { "productId": "featurebase", "storyId": "reschedule-initiatives", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "The evidence pack shows Featurebase has roadmap, changelog, and release notes features, but there is no mention of rescheduling initiatives or moving items between releases as priorities shift. missing for 10: any documentation of drag-and-drop or timeline rescheduling, release reassignment, or initiative status/date change functionality.", "evidenceIds": [] }, { "productId": "featurebase", "storyId": "surface-related-feature-insights", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Featurebase claims AI helps 'declutter & find the most impactful opportunities' (docs-7) and offers AI search answers (docs-14) and an AI support agent (docs-10), suggesting some AI-driven feedback analysis, but there is no explicit evidence of automatic insight detection or automatic linking of feedback to related feature ideas. Missing for 10: documentation of duplicate/similarity detection, insight extraction from feedback text, and automatic linking mechanics to roadmap items.", "evidenceIds": [ "featurebase-docs-7", "featurebase-docs-14", "featurebase-docs-10" ] }, { "productId": "featurebase", "storyId": "targeted-release-notes", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Featurebase explicitly advertises segmenting product release announcements by user attributes for targeted release notes, directly matching the story, plus supporting notification automation for shipped requests. Missing for 10: independent/hands-on corroboration of this specific segmentation feature and technical detail on how attribute-based targeting is configured.", "evidenceIds": [ "featurebase-docs-6", "featurebase-docs-12" ] }, { "productId": "featurebase", "storyId": "track-task-progress", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows Featurebase has status change notifications and webhooks for feedback/roadmap items, but nothing describes per-task 'blocked on' fields or granular real-time task status views expected by a PM tracking in-progress work. missing for 10: any mention of blocked-status tracking, task-level detail views, or real-time in-progress dashboards.", "evidenceIds": [ "featurebase-docs-1", "featurebase-docs-12" ] }, { "productId": "featurebase", "storyId": "usage-analytics-adoption", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack covers webhooks, integrations, changelogs, help center, and AI agents, but contains no mention of an analytics/reporting feature for tracking product adoption or usage across a workspace. Since Featurebase is a feedback/roadmap platform where usage analytics could plausibly be a feature, the lack of any supporting evidence yields 'none' rather than 'na'.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "agentic-agent-docs", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "A probe confirms Productboard serves a working llms.txt at developer.productboard.com/llms.txt (HTTP 200) with a description of the API and instructions to append .md to any doc page for agent-readable versions, which is exactly the agent-oriented docs pattern the story asks for. Missing for 10: independent/community confirmation that agents actually consume this llms.txt successfully in practice.", "evidenceIds": [ "productboard-probe-1" ] }, { "productId": "productboard", "storyId": "agentic-ai-insights", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Productboard automatically detects insights within feedback and links them to related feature ideas, showing native AI-generated insight surfacing inside the product, and the MCP server lets AI agents act on that data (prioritization, specs, initiatives). However, evidence is limited to marketing claims with no hands-on validation of insight quality or breadth of AI suggestions beyond feedback-to-feature linking. Missing for 10: independent/hands-on verification of AI insight accuracy, broader AI-generated suggestions (e.g., roadmap prioritization, summarization) beyond feedback linking, and detail on how insights are surfaced in UI.", "evidenceIds": [ "productboard-docs-16", "productboard-docs-10", "productboard-docs-11", "productboard-docs-13" ] }, { "productId": "productboard", "storyId": "agentic-autonomous-automation", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Productboard exposes webhooks, a REST API, and an MCP server that let external agents or scripts react to events and modify data, which are the building blocks for automation, but there's no documented native feature for scheduling or running autonomous background workflows within Productboard itself — all examples show agent-initiated or event-triggered actions rather than self-running background automations. missing for 10: a first-party 'automation' or scheduled-agent feature, evidence of unattended/recurring execution, and confirmation that MCP-driven actions can run without a live agent session.", "evidenceIds": [ "productboard-docs-5", "productboard-docs-10", "productboard-docs-11", "productboard-docs-13", "productboard-probe-3" ] }, { "productId": "productboard", "storyId": "agentic-builtin-assistant", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence covers an MCP server that lets external AI agents (Claude, ChatGPT, coding agents) call into Productboard's data and workflows, plus automatic insight detection — but nothing describes a built-in, in-product AI assistant/chat that a user can delegate tasks to. MCP is an external-agent connector, not an assistant embedded in Productboard's own UI, and insight-detection is automatic, not user-delegated task execution.", "evidenceIds": [ "productboard-docs-10", "productboard-docs-11", "productboard-docs-12", "productboard-docs-16", "productboard-probe-3" ] }, { "productId": "productboard", "storyId": "agentic-headless", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Productboard's REST API supports personal-access-token authentication, cursor-based pagination, and webhooks, all of which enable scripted/headless use in CI or automation pipelines (docs-43, docs-51, docs-5). However, there is no explicit documentation of CI/CD integration, SDKs, or example automation scripts targeting headless pipelines. Missing for 10: explicit CI/CD pipeline examples, official SDK/CLI for automation, and documented rate-limit/queueing guidance for unattended batch jobs.", "evidenceIds": [ "productboard-docs-43", "productboard-docs-1", "productboard-docs-3", "productboard-docs-5", "productboard-docs-51" ] }, { "productId": "productboard", "storyId": "agentic-mcp-client", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "All evidence describes Productboard shipping its own MCP server so external agents/tools can call Productboard's capabilities (docs-10 through docs-13, probe-3) — this is the reverse role. There is no evidence that Productboard itself can act as an MCP client and plug in external MCP servers to use their tools.", "evidenceIds": [ "productboard-docs-10", "productboard-docs-11", "productboard-docs-12", "productboard-docs-13", "productboard-probe-3" ] }, { "productId": "productboard", "storyId": "agentic-mcp-server", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "Productboard ships an official, well-documented MCP server with a single connection endpoint (mcp.productboard.com), browser-based sign-in via existing login, OAuth with PKCE/Dynamic Client Registration, admin controls for enabling MCP and role-based access, and documented use cases for reading/writing features, initiatives, specs, and prioritization data via coding agents and AI tools. Missing for 10: independent/third-party hands-on corroboration beyond vendor docs.", "evidenceIds": [ "productboard-docs-37", "productboard-docs-38", "productboard-docs-39", "productboard-docs-41", "productboard-docs-50", "productboard-docs-19", "productboard-intdir-1", "productboard-probe-3" ] }, { "productId": "productboard", "storyId": "agentic-nl-commands", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Productboard ships an official MCP server (mcp.productboard.com) plus Spark natural-language interface that let any AI agent (Claude, ChatGPT, coding agents) read specs and create/update features, initiatives, releases, and objectives via natural language, and Spark supports natural-language queries against Slack/Amplitude/Notion. This is first-party documented and includes install/auth/OAuth flows and worked examples. Missing for 10: independent/hands-on third-party verification of the natural-language command experience and coverage of edge cases or limitations in real usage.", "evidenceIds": [ "productboard-docs-10", "productboard-docs-19", "productboard-docs-37", "productboard-docs-38", "productboard-docs-40", "productboard-intdir-1", "productboard-intdir-6", "productboard-intdir-8", "productboard-probe-3" ] }, { "productId": "productboard", "storyId": "agentic-official-cli", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Productboard documents a REST API, webhooks, and an MCP server, but no evidence anywhere mentions an official CLI tool for AI-native workflows.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "agentic-public-api", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "Productboard documents a comprehensive REST API v2 with authentication (token and OAuth PKCE), cursor-based pagination, CRUD across features, feedback, documents, customers, teams, webhooks, and configuration/discovery endpoints, plus quickstart and Postman guides, and an llms.txt probe confirms machine-readable docs are live. This is a thorough, well-documented public API suitable for AI-native/agent-driven use, reinforced by a dedicated MCP server for agent access. missing for 10: no independent/third-party corroboration of real-world API usage or hands-on developer confirmation beyond vendor docs, and an OpenAPI spec file was not found at expected locations.", "evidenceIds": [ "productboard-docs-1", "productboard-docs-3", "productboard-docs-27", "productboard-docs-28", "productboard-docs-43", "productboard-docs-44", "productboard-docs-51", "productboard-docs-35", "productboard-docs-36", "productboard-probe-1", "productboard-probe-2" ] }, { "productId": "productboard", "storyId": "agentic-scoped-keys", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Productboard offers multiple credential types for agents (personal access tokens, OAuth 2.0 authorization-code flow with consent screens, and PKCE-based OAuth for MCP clients) and lets admins restrict which roles may connect an agent, giving some coarse-grained control over agent access. However, there is no documented support for fine-grained scopes (e.g., read-only vs write, per-resource permissions) on tokens or OAuth grants, so true least-privilege credential issuance isn't evidenced. Missing for 10: explicit scoped/permission-limited token or OAuth scope configuration, documentation of granular read/write or resource-level access control for agent credentials, and independent confirmation of scope enforcement.", "evidenceIds": [ "productboard-docs-39", "productboard-docs-41", "productboard-docs-43", "productboard-docs-44", "productboard-docs-38" ] }, { "productId": "productboard", "storyId": "agentic-sdks", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Productboard publishes a well-documented REST API v2 (quickstart, Postman collection, auth via PAT/OAuth, pagination, webhooks) and an MCP server for agent integration, which lets AI-native builders programmatically interact with the product. However, there's no evidence of official language-specific client SDKs (e.g., Python/JS packages) and the OpenAPI spec probe returned 404s, suggesting no auto-generatable SDK artifact. Missing for 10: official SDK packages in common languages, published OpenAPI/swagger spec, independent developer confirmation of SDK usage.", "evidenceIds": [ "productboard-docs-36", "productboard-docs-35", "productboard-docs-43", "productboard-docs-44", "productboard-probe-2", "productboard-docs-37", "productboard-docs-38" ] }, { "productId": "productboard", "storyId": "agentic-webhooks", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Productboard documents a dedicated Webhooks API with explicit create/list/delete endpoints for subscribing to entity change events and receiving real-time notifications, matching the story directly. Missing for 10: independent/hands-on corroboration of webhook reliability and payload/event-type documentation depth beyond the reference stubs.", "evidenceIds": [ "productboard-docs-5", "productboard-docs-32", "productboard-docs-33", "productboard-docs-34" ] }, { "productId": "productboard", "storyId": "ai-declutter-feedback", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Productboard's docs claim automatic AI-driven insight detection linking feedback to feature ideas and a centralized searchable feedback repository, which directly supports decluttering raw feedback, and its MCP/API layer lets automation update prioritization fields (value, risk, impact, effort). However, there is no detailed documentation of an AI feature that explicitly ranks or surfaces 'most impactful opportunities' beyond basic insight linking, and no independent/hands-on evidence validating this capability. Missing for 10: dedicated AI opportunity-scoring/ranking feature documentation, independent user validation of insight quality, and clearer detail on how 'impactful' is determined.", "evidenceIds": [ "productboard-docs-16", "productboard-docs-15", "productboard-docs-14", "productboard-docs-11" ] }, { "productId": "productboard", "storyId": "ai-draft-task-from-feedback", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Productboard's auto-insight detection links feedback to feature ideas (docs-16) and its MCP server lets AI agents create features/initiatives from context (docs-10–13), which is adjacent to turning feedback into structured work items, but there is no explicit documentation of AI parsing a plain-language feedback description into a distinct, structured set of proposed tasks. missing for 10: explicit feature/doc showing AI decomposing a single feedback item into multiple structured task proposals, evidence of task granularity (subtasks, acceptance criteria), independent/hands-on confirmation of this specific workflow.", "evidenceIds": [ "productboard-docs-16", "productboard-docs-10", "productboard-docs-11", "productboard-docs-13" ] }, { "productId": "productboard", "storyId": "api-interactive-docs", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence confirms Productboard has API documentation (developer.productboard.com, llms.txt) but no mention of an interactive reference with runnable/try-it examples, and probes for an OpenAPI/Swagger spec (which typically power such consoles) all returned 404. Missing for 10: any documentation or screenshot of an interactive console, runnable code snippets, or Swagger/Redoc-style UI.", "evidenceIds": [ "productboard-probe-1", "productboard-probe-2" ] }, { "productId": "productboard", "storyId": "api-machine-spec", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "While Productboard documents a REST API v2 and even a Postman quickstart, no first-party page offers a downloadable OpenAPI/Swagger file, and a direct probe of the standard OpenAPI spec locations (openapi.json, swagger.json, etc.) on developer.productboard.com all returned 404, indicating no machine-readable spec is actually published for retrieval.", "evidenceIds": [ "productboard-probe-2", "productboard-docs-35", "productboard-docs-36" ] }, { "productId": "productboard", "storyId": "api-sandbox", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence pack item mentions a sandbox, staging, test workspace, or non-production environment for API/MCP testing; all documentation refers to production workspace access and live data operations.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "api-versioning-policy", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "Evidence describes REST API features, pagination, webhooks, and MCP server capabilities but contains no mention of API versioning scheme or a documented deprecation policy; OpenAPI spec probes also 404, offering no discoverable version metadata.", "evidenceIds": [ "productboard-probe-2" ] }, { "productId": "productboard", "storyId": "approve-task-contract", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Productboard's docs mention gating delivery on 'fully-validated features' and using specs to 'initiate the product delivery process', implying a review step before work begins, but there's no explicit evidence of a formal approval/sign-off workflow, status gate, or scope-lock mechanism a PM would use to approve a task. missing for 10: explicit approval/status workflow documentation, evidence of a sign-off step or scope lock before handoff to engineering, independent corroboration of this gating in practice.", "evidenceIds": [ "productboard-docs-14", "productboard-docs-13", "productboard-docs-15" ] }, { "productId": "productboard", "storyId": "automated-sla-workflows", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Productboard offers generic Webhooks/REST APIs for feedback events, but there is no evidence of built-in or documented workflow automation for SLAs, trial extensions, or discount offers — these are outside Productboard's feedback/roadmap product scope and no such workflow builder or triggers are described.", "evidenceIds": [ "productboard-docs-5", "productboard-docs-1" ] }, { "productId": "productboard", "storyId": "automated-status-updates", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Productboard exposes a Webhooks API that fires real-time notifications when entity data (e.g., feature status) changes, and feedback entries can be linked to customers, so a PM could build an automation to notify requesters. However, there is no evidence of a native, built-in 'notify customer when feature status changes' feature — it would require custom integration work using the webhook/API to reach customers. Missing for 10: a documented native customer-notification feature (e.g., automated customer emails on status change), evidence of end-to-end customer messaging rather than just internal event triggers, and any hands-on confirmation this workflow is used in practice.", "evidenceIds": [ "productboard-docs-5", "productboard-docs-32", "productboard-docs-33", "productboard-docs-4", "productboard-docs-47" ] }, { "productId": "productboard", "storyId": "automation-bulk-operations", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Productboard's REST API supports cursor-based pagination for list/search endpoints and explicitly mentions 'bulk operations' for document management, plus general create/update/delete across features, feedback, and other entities that an AI-native user (via scripts or MCP-connected agents) could loop through for bulk actions. However, there's no documented native batch/bulk-update endpoint (e.g., a single call to update many items at once) outside of the one document-specific mention, and the MCP server examples focus on single-item actions rather than explicit multi-item bulk operations. Missing for 10: dedicated bulk-update/bulk-create API endpoints across all entity types, explicit bulk-operation support in the MCP tool set, and independent evidence of an AI agent performing true bulk operations at scale.", "evidenceIds": [ "productboard-docs-30", "productboard-docs-3", "productboard-docs-28", "productboard-docs-51", "productboard-docs-19", "productboard-docs-37" ] }, { "productId": "productboard", "storyId": "automation-rules-engine", "verdict": "partial", "quality": 4, "confidence": "medium", "rationale": "Productboard's Webhooks API lets users subscribe to entity-change events and receive real-time notifications, and the REST/MCP APIs allow subsequent actions to be scripted in response — but this requires building custom integration code rather than a native declarative 'if this event then this action' rules engine inside the product. Missing for 10: evidence of a built-in no-code automation/rules builder, conditional logic across multiple triggers, and any UI for defining action chains without custom webhook-consumer code.", "evidenceIds": [ "productboard-docs-5", "productboard-docs-32", "productboard-docs-33", "productboard-docs-34", "productboard-docs-19" ] }, { "productId": "productboard", "storyId": "automation-scheduled-jobs", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Evidence shows Productboard has webhooks (event-driven push), a REST API, and an MCP server for agent-driven actions, but nothing describes scheduling recurring jobs, cron-like automation, or timed/repeating workflows. The axis applies (automation platforms commonly offer this) but no supporting evidence exists.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "automation-versioned-workflows", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack covers Productboard's API, webhooks, MCP server, and integrations for reading/writing product data, but there is no mention of any versioning, review, or rollback mechanism for automations, workflows, or agent actions—no changelog, audit history, undo, or approval-based automation review capability is documented anywhere.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "branded-changelog-page", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence in the pack mentions a public changelog feature, custom domain hosting, or branding for a changelog page; all evidence concerns API, webhooks, MCP server, and integrations. This is a plausible feature for a product management tool (Productboard historically has a Portal/changelog-adjacent feature area), so the axis applies, but nothing in the evidence supports it.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "capture-feedback-from-conversations", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Productboard's API/webhooks let feedback be routed in from CRMs, internal tools, and other sources, and it offers a single searchable feedback repository with automatic insight detection, but there's no direct evidence of native, purpose-built connectors that auto-capture from sales calls, support tickets, or chat tools (e.g. Gong, Zendesk, Intercom) — only generic API-based ingestion is documented. missing for 10: named native integrations for sales-call/support-ticket/chat capture, evidence of automatic (not developer-built) capture pipelines, and independent confirmation of these integrations working in practice.", "evidenceIds": [ "productboard-docs-1", "productboard-docs-2", "productboard-docs-4", "productboard-docs-15", "productboard-docs-16" ] }, { "productId": "productboard", "storyId": "centralized-feedback-repository", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "Productboard's own docs explicitly state the exact capability (\"Access one searchable repository for all product requests and feedback from colleagues and customers\"), reinforced by feedback API features supporting associating feedback with users/companies and searching across it. missing for 10: independent/hands-on corroboration beyond vendor docs.", "evidenceIds": [ "productboard-docs-15", "productboard-docs-4", "productboard-docs-16" ] }, { "productId": "productboard", "storyId": "changelog-email-subscriptions", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack covers Productboard's API, webhooks, MCP server, and integrations, but contains no mention of a customer-facing changelog feature or an email subscription mechanism for changelog updates. This is a plausible feature for a product-management tool, so the axis applies, but no evidence supports it.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "connect-support-tools", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Productboard's API and webhook capabilities (docs-1, docs-2, docs-5) let feedback be routed in from CRMs and other external systems, and custom integrations can be built into the UI, which technically enables connecting support/sales/chat data sources. However, the evidence pack contains no mention of specific pre-built native connectors for common support, sales, or chat tools (e.g., named integrations), only generic REST/webhook/custom-integration capability. Missing for 10: named native integrations with support/sales/chat platforms, evidence of a marketplace/app directory, and independent confirmation of ease-of-setup for non-technical PMs.", "evidenceIds": [ "productboard-docs-1", "productboard-docs-2", "productboard-docs-5", "productboard-docs-7" ] }, { "productId": "productboard", "storyId": "convert-feedback-to-task", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Productboard has a core feedback repository that automatically detects insights linkable to feature ideas, notes can be linked to features to quantify demand, and features can be pushed into delivery/tracked—directly supporting turning feedback into actionable tasks/initiatives. This is reinforced by API/MCP capabilities to create feedback, link it to features/initiatives, and push prioritized features into delivery. Missing for 10: independent/hands-on verification of the feedback-to-feature workflow UI and any case-study evidence of PM usage in practice.", "evidenceIds": [ "productboard-docs-16", "productboard-docs-47", "productboard-docs-14", "productboard-docs-20", "productboard-docs-15", "productboard-docs-4", "productboard-docs-27" ] }, { "productId": "productboard", "storyId": "customer-context-for-prioritization", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Productboard associates feedback with users/companies and links notes to features to quantify demand, provides a single searchable repository for requests/feedback, and surfaces automatic insight detection with citations tying feedback to evidence, directly supporting customer-context-informed prioritization. missing for 10: independent/hands-on validation from PM users of the customer-context view and quantitative proof of prioritization impact.", "evidenceIds": [ "productboard-docs-4", "productboard-docs-14", "productboard-docs-15", "productboard-docs-16", "productboard-docs-21", "productboard-docs-47" ] }, { "productId": "productboard", "storyId": "customize-widget-appearance", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence in the pack addresses a feedback widget for capturing customer input, let alone branding customization (label/color) for it; all evidence covers API, MCP, and third-party integrations.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "docs-editor-for-announcements", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack mentions 'editor documents' and document management via the API, but nothing describes a rich text editor with custom components, nor any changelog or help-content authoring feature for product managers. Missing for 10: any documentation of a WYSIWYG/rich text editor, custom embeddable components, or changelog/help-center publishing capability.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "embed-feedback-widget", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of an embeddable feedback widget with a script tag; the evidence pack only covers Productboard's REST API, webhooks, MCP server, and internal feedback repository features, none of which describe a client-side embed widget.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "embed-integrations-in-ui", "verdict": "partial", "quality": 6, "confidence": "low", "rationale": "First-party docs explicitly state you can 'build custom integrations that appear and operate inside the Productboard UI' (docs-7) and describe an API/webhooks platform for extending Productboard (intdir-10, docs-5, docs-31), which supports the core claim. However, there's no detail on an actual UI-extension SDK, embed points, or hands-on/independent confirmation of what such an in-UI integration looks like in practice. Missing for 10: concrete UI-extension developer docs/SDK, third-party example integrations that embed in the Productboard UI, and independent corroboration beyond vendor marketing copy.", "evidenceIds": [ "productboard-docs-7", "productboard-intdir-10", "productboard-docs-5", "productboard-docs-31", "productboard-docs-46" ] }, { "productId": "productboard", "storyId": "embeddable-changelog-widget", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Productboard's evidence pack covers REST API, webhooks, MCP server, and integrations for feedback/roadmap data, but there is no mention of a public-facing embeddable changelog widget for showcasing product updates to end users. This is a plausible feature for a product management tool (e.g., similar to 'What's New' widgets), but no evidence shows it exists.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "identify-users-in-feedback", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Productboard's API explicitly supports associating feedback with existing users and companies, and docs mention routing feedback from CRMs/internal tools into user records (productboard-docs-4, productboard-docs-2), which implies developers can tie submitted feedback to an existing account. However, there is no evidence of a specific mechanism for identifying a 'logged-in' end user at submission time (e.g., SSO/JWT-based identification in a feedback widget or portal). Missing for 10: documentation of end-user authentication/identification flow for feedback submission, widget/portal-specific user linking, and independent confirmation of this behavior.", "evidenceIds": [ "productboard-docs-4", "productboard-docs-2" ] }, { "productId": "productboard", "storyId": "jump-to-item-by-shortname", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence describes a quick-open or 'jump to' navigation feature where a developer types a short name/ID to instantly open a feedback item, task, or initiative. Evidence only covers retrieving entity IDs via the API/UI (docs-46) and general search repositories (docs-15), which is a different capability.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "link-feedback-to-customers", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Productboard's API and product docs explicitly support associating feedback with customers/companies, with dedicated attribution fields and relationship endpoints (create/list/delete), plus UI-level customer attribution on feedback capture. missing for 10: no independent/hands-on corroboration of the linking UX beyond vendor docs, and no explicit detail on how attribution surfaces in the feedback list/search UI.", "evidenceIds": [ "productboard-docs-4", "productboard-docs-27", "productboard-docs-29", "productboard-docs-15" ] }, { "productId": "productboard", "storyId": "link-feedback-to-roadmap-items", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Productboard's core product model explicitly links feedback/notes to features, and the API/MCP docs confirm associating feedback with features, adding features to initiatives, and moving initiatives to releases. This is a core, well-documented native capability, not just a workaround via API. missing for 10: independent/hands-on user corroboration of the linking workflow in the UI (all evidence is vendor documentation).", "evidenceIds": [ "productboard-docs-4", "productboard-docs-12", "productboard-docs-47", "productboard-docs-29", "productboard-docs-16", "productboard-docs-19" ] }, { "productId": "productboard", "storyId": "link-spec-to-delivery", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Productboard's MCP server explicitly lets coding agents and AI prototyping tools pull approved specs directly into the delivery workflow ('Access your latest product specifications from coding agents... to initiate the product delivery process', 'Pull a spec, talk through the plan with your agent, and build'), plus GitHub/Jira push-to-dev integrations and a REST API for programmatic access. Missing for 10: independent/hands-on developer confirmation that the workflow works smoothly in practice beyond vendor docs.", "evidenceIds": [ "productboard-docs-10", "productboard-docs-13", "productboard-docs-37", "productboard-docs-40", "productboard-docs-20", "productboard-intdir-4", "productboard-docs-14" ] }, { "productId": "productboard", "storyId": "link-to-feedback-site", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "The evidence pack covers Productboard's API, webhooks, MCP server, and third-party integrations, but contains no mention of a customer-facing feedback Portal or an embeddable link/widget that developers could add to their product to direct users to submit feedback. This capability is plausible for a product-feedback tool but is simply unevidenced here.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "one-click-integration-setup", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Evidence focuses on API/webhooks/MCP server for building custom integrations, which requires developer effort rather than a few-click connection for a PM. There's no mention of a pre-built integrations marketplace or one-click connectors (e.g., Slack, Jira, Salesforce) that a non-technical PM could enable directly. Missing for 10: evidence of a native integrations directory/marketplace with one-click setup, no-code connector examples, and PM-facing (not developer-facing) setup flow.", "evidenceIds": [ "productboard-docs-1", "productboard-docs-2", "productboard-docs-7", "productboard-probe-3" ] }, { "productId": "productboard", "storyId": "openness-api-parity", "verdict": "partial", "quality": 7, "confidence": "medium", "rationale": "Productboard's REST API (v2) and MCP server cover most core objects—features, initiatives, releases, objectives, key results, feedback, documents, teams, companies, users, webhooks, and custom fields—letting AI agents create/update/query nearly all major workspace entities also editable in the UI. However, there's no discoverable OpenAPI/swagger spec (probe returned 404s), no vendor claim of 100% UI-API parity, and some UI-only workflows (e.g., insights auto-detection, Spark synthesis agent, deep configuration/admin settings) aren't clearly exposed via API. Missing for 10: a published complete OpenAPI schema, explicit parity statement, and independent confirmation that all UI actions (not just major entities) are API-accessible.", "evidenceIds": [ "productboard-docs-1", "productboard-docs-4", "productboard-docs-8", "productboard-docs-17", "productboard-docs-19", "productboard-docs-30", "productboard-docs-5", "productboard-docs-48", "productboard-docs-49", "productboard-probe-2", "productboard-docs-37" ] }, { "productId": "productboard", "storyId": "openness-full-export", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Productboard's REST API v2 lets users programmatically read/list virtually all entities (feedback, features, documents, customers, teams) with cursor-based pagination, which effectively enables a full data export via JSON over HTTP — an open, non-proprietary format and mechanism to leave with your data intact. However, there is no documented one-click 'export all my data' feature, no explicit mention of bulk/GDPR-style data export or account-closure export guarantee, and no confirmation of formats beyond implied JSON via REST responses. Missing for 10: a dedicated bulk/full-account export tool, explicit open-format guarantees (e.g., CSV/JSON export docs), and evidence of a data-portability/account-deletion export flow.", "evidenceIds": [ "productboard-docs-1", "productboard-docs-3", "productboard-docs-27", "productboard-docs-28", "productboard-docs-30", "productboard-docs-51" ] }, { "productId": "productboard", "storyId": "openness-open-license", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Productboard is a closed, commercial SaaS product; nothing in the evidence suggests its source code is open or licensed for public inspection. Reading source under an open license is not a fair axis for a proprietary SaaS platform.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "openness-self-host", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Productboard is a SaaS-only cloud product; no evidence anywhere in the pack mentions self-hosting, on-prem deployment, or a self-managed core product. Absence of evidence for an applicable capability means 'none'.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "prioritization-scoring-fields", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Productboard's docs explicitly reference custom fields/drivers such as value, risk, impact, confidence, and effort on features/initiatives, and both the product data model and MCP server allow updating these to drive prioritization workflows. Missing for 10: no independent/hands-on corroboration of a dedicated scoring UI or formulaic scoring model, and no direct screenshot/first-party doc showing all five named fields together in a native scorecard.", "evidenceIds": [ "productboard-docs-11", "productboard-docs-4", "productboard-docs-14" ] }, { "productId": "productboard", "storyId": "privacy-data-residency", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack addresses data residency, region selection, or storage location controls; all evidence concerns API, MCP, and integration capabilities. Missing for 10: any mention of data residency options, EU/US hosting choice, or compliance documentation addressing storage location.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "privacy-no-training", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack addresses AI/model-training data usage, opt-out controls, or any privacy policy statement about training on customer data; all evidence concerns API/MCP/integration functionality, not data-training privacy posture.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "privacy-retention-controls", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack covers API/webhook/MCP capabilities extensively but contains no mention of data retention policies, deletion of stored customer/product data for privacy purposes, GDPR/right-to-be-forgotten controls, or data export/purge tooling. This axis is applicable to a SaaS platform storing customer feedback data, but no supporting evidence exists.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "privacy-telemetry-optout", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack addresses telemetry opt-out or usage tracking controls for end users; the only related item is an Analytics API describing workspace usage data for admins, not a user-facing telemetry opt-out mechanism.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "product-hierarchy-management", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Productboard's core product model natively supports organizing feedback/requests into features, subfeatures, components, initiatives, releases, objectives and key results, with API and MCP evidence confirming these hierarchy entities and the ability to link feedback and features to them (e.g., docs-4, docs-19, docs-29, docs-12, docs-47). This directly matches the story of organizing requests into a feature/component/initiative hierarchy. Missing for 10: independent/hands-on corroboration beyond vendor docs and a clearer description of the component-level UI hierarchy itself (most detail is API/MCP-centric rather than native UI workflow).", "evidenceIds": [ "productboard-docs-4", "productboard-docs-19", "productboard-docs-29", "productboard-docs-12", "productboard-docs-47", "productboard-docs-15" ] }, { "productId": "productboard", "storyId": "public-feedback-portal", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack covers Productboard's API, webhooks, MCP server, and internal feedback repository, but contains no mention of a public-facing customer portal where customers submit and vote on ideas. Missing for 10: any documentation of a public portal, idea submission form, or voting mechanism for customers.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "publish-changelog", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "While Productboard emphasizes closing the loop between specification, delivery, and outcome (productboard-docs-23) and offers rich APIs/integrations for pushing data to delivery tools, there is no evidence of a dedicated changelog/release-notes publishing feature for announcing shipped features to customers. Missing for 10: any mention of a customer-facing changelog or release-notes publishing tool, templates, or distribution channel (email, in-app, public page).", "evidenceIds": [] }, { "productId": "productboard", "storyId": "query-feedback-themes-via-ai", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Productboard's MCP server lets AI agents create/update features and initiatives, and it auto-detects insights linking feedback to features, suggesting some AI-native querying capability over feedback data. However, there is no evidence of an AI assistant answering natural-language questions about feedback themes or their revenue impact—the Analytics API is scoped to workspace usage metrics, not customer feedback/revenue correlation. Missing for 10: evidence of a conversational AI/analytics feature that surfaces feedback theme summaries, revenue impact metrics, or any linkage between feedback and revenue data.", "evidenceIds": [ "productboard-docs-10", "productboard-docs-16", "productboard-docs-9", "productboard-probe-3" ] }, { "productId": "productboard", "storyId": "quick-portal-setup", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack covers APIs, webhooks, MCP server, and integrations, but contains no mention of a self-service public feedback portal feature that a PM could configure without engineering help.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "reply-to-customers-inline", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence covers feedback capture, tagging, linking to features, and API/webhook management, but nothing describes a customer-facing reply mechanism from within a feedback record. The 'close the loop' mention (docs-23) refers to specification-to-shipped tracking, not customer communication, so this specific capability is unevidenced.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "reschedule-initiatives", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Productboard's core roadmap model explicitly supports releases and initiatives, and both docs and MCP-server pages state you can 'reschedule an initiative' and 'move an initiative to a new release' directly, including via API/agent workflows. This is native product functionality (not just a third-party workaround), covering the exact story described.\n\nmissing for 10: no independent/hands-on UI walkthrough or screenshot showing the drag-and-drop reschedule experience in the roadmap view, only docs/MCP references.", "evidenceIds": [ "productboard-docs-10", "productboard-docs-12", "productboard-docs-19", "productboard-docs-49" ] }, { "productId": "productboard", "storyId": "surface-related-feature-insights", "verdict": "partial", "quality": 6, "confidence": "low", "rationale": "Productboard's marketing docs explicitly claim automatic insight detection linked to feature ideas, with citations for findings, but this is described only at a high marketing level with no technical documentation, independent validation, or hands-on evidence of accuracy/scope. Missing for 10: technical/API documentation of the insight-detection mechanism, independent or hands-on verification of accuracy, and detail on how linking to feature ideas actually works in practice.", "evidenceIds": [ "productboard-docs-16", "productboard-docs-21", "productboard-docs-22" ] }, { "productId": "productboard", "storyId": "targeted-release-notes", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows release grouping and general product data management, but nothing about segmenting or sending targeted release-note announcements based on user attributes. Productboard's docs cover feedback, integrations, MCP server, and API endpoints, none of which address customer-facing release note personalization or segmentation.", "evidenceIds": [] }, { "productId": "productboard", "storyId": "track-task-progress", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Productboard's evidence covers pushing prioritized features into delivery tools and syncing status/key data (e.g., docs-20, docs-23), but there is no evidence of a real-time view showing what a specific in-progress task is blocked on or its last update timestamp — that granularity lives in delivery tools like Jira that Productboard integrates with, not in Productboard itself.", "evidenceIds": [ "productboard-docs-20", "productboard-docs-23", "productboard-intdir-2", "productboard-intdir-4" ] }, { "productId": "productboard", "storyId": "usage-analytics-adoption", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Productboard explicitly documents an Analytics API providing usage data on adoption across the workspace, and notes this can feed internal dashboards, directly matching the story. However, there is no first-party dashboard/report UI evidence, no sample metrics/fields list, and no independent corroboration of what analytics are actually surfaced. missing for 10: details on specific adoption metrics tracked, a native analytics dashboard UI (not just API), and independent/hands-on validation of the Analytics API's output.", "evidenceIds": [ "productboard-docs-9", "productboard-docs-18" ] } ]