Rank #4 of 5 in Incident Management & On-call
Showcase


Try itExperimental
See what an agent can do with FireHydrant before you ever sign up. Pick a story: recorded sessions replay real probe-harness transcripts; sandboxed self-drive sessions are designed and gated (docs/TRY-IT.md).
$printf '<jsonrpc initialize>' | npx -y firehydrant-mcp start --transport stdio # full keyless stdio handshakerecorded session — replayed, not liveVerified integrations
Connections to other tracked products — hover a chip for the verbatim evidence quote behind it.
By theme — the product's score on each story themeBy theme
Agenticness — how well agents can access and operate the productAgenticnessevidence →
How well agents can access and operate the product
Ai incident — stories about ai incident in this arenaAi incidentevidence →
Stories about ai incident in this arena
Alerting escalation — stories about alerting escalation in this arenaAlerting escalationevidence →
Stories about alerting escalation in this arena
Analytics reliability — stories about analytics reliability in this arenaAnalytics reliabilityevidence →
Stories about analytics reliability in this arena
Automation depth — how much of the product can run unattendedAutomation depthevidence →
How much of the product can run unattended
Automation runbooks — stories about automation runbooks in this arenaAutomation runbooksevidence →
Stories about automation runbooks in this arena
Incident response — stories about incident response in this arenaIncident responseevidence →
Stories about incident response in this arena
Integrations observability — stories about integrations observability in this arenaIntegrations observabilityevidence →
Stories about integrations observability in this arena
Mobile experience — stories about mobile experience in this arenaMobile experienceevidence →
Stories about mobile experience in this arena
On call scheduling — stories about on call scheduling in this arenaOn call schedulingevidence →
Stories about on call scheduling in this arena
Openness — open source, data portability, and self-hosting storiesOpennessevidence →
Open source, data portability, and self-hosting stories
Postmortems learning — stories about postmortems learning in this arenaPostmortems learningevidence →
Stories about postmortems learning in this arena
Privacy posture — data-handling and privacy storiesPrivacy postureevidence →
Data-handling and privacy stories
Status communication — stories about status communication in this arenaStatus communicationevidence →
Stories about status communication in this arena
Story verdicts — every judged story with its evidenceStory verdicts
What’s free: 0 free · 0 paid · 1 enterprise · 30 not stated in evidence
Follow the green: where the map greys out is where FireHydrant stops today. ✓ full · ~ partial · ! disputed · — none · n/a not applicable.
Agenticness — how well agents can access and operate the productAgenticness
How well agents can access and operate the product
API surface
Drive the product through a documented public API
✓8/10
unlocks → Official SDKs · Scoped API keys · Machine-readable spec · Versioning policy · API sandbox · Official CLI · Full data export · First-party integrations cover my observability stack — Datadog, Grafana, Prometheus, CloudWatch, Sentry — with documented setup
Subscribe to events via webhooks
~3/10
Build against official SDKs
—0/10
Issue scoped/least-privilege API credentials for an agent
—0/10
Connect an agent via an official MCP server
✓8/10
Download a machine-readable API spec (OpenAPI or equivalent)
—0/10
Rely on versioned APIs with a documented deprecation policy
—0/10
Test against a sandbox environment without touching production data
—–
Explore an interactive API reference with runnable examples
—0/10
Docs for agents
Point an agent at llms.txt or agent-oriented docs
✓8/10
Agentic features
Delegate tasks to a built-in AI assistant inside the product
~5/10
unlocks → MCP client
Operate the product with natural-language commands
~6/10
Plug MCP servers into this product so it can use their tools
—0/10
Get AI-generated insights and suggestions from my data inside the product
✓8/10
Set up automations that run autonomously in the background
~6/10
Ai incident — stories about ai incident in this arenaAi incident
Stories about ai incident in this arena
Alerting escalation — stories about alerting escalation in this arenaAlerting escalation
Stories about alerting escalation in this arena
Escalation policies walk unacknowledged pages through multiple steps — delays, fallback responders, and repeat rounds — until someone acknowledges
~5/10
Duplicate and related alerts are deduplicated and grouped so one incident pages one human, not fifty
—0/10
Pages reach me over the channels I choose — push, SMS, phone call, and email — with per-channel notification rules
~3/10
Alerts from my monitoring tools are ingested through documented sources and routed to the right team by conditions I define
✓8/10
Analytics reliability — stories about analytics reliability in this arenaAnalytics reliability
Stories about analytics reliability in this arena
Automation depth — how much of the product can run unattendedAutomation depth
How much of the product can run unattended
Automation runbooks — stories about automation runbooks in this arenaAutomation runbooks
Stories about automation runbooks in this arena
Incident response — stories about incident response in this arenaIncident response
Stories about incident response in this arena
Internal stakeholders get structured incident updates they can subscribe to, without joining the war room
~7/10
Incidents carry defined roles (commander, comms lead) and task checklists so response stays coordinated under pressure
✓9/10
Declare and run an incident from chat — Slack or Teams — with channels, roles, and updates created for me
~6/10
The incident timeline is captured automatically — alerts, actions, and chat decisions — and I can edit or annotate it afterwards
~6/10
Integrations observability — stories about integrations observability in this arenaIntegrations observability
Stories about integrations observability in this arena
Mobile experience — stories about mobile experience in this arenaMobile experience
Stories about mobile experience in this arena
On call scheduling — stories about on call scheduling in this arenaOn call scheduling
Stories about on call scheduling in this arena
Openness — open source, data portability, and self-hosting storiesOpenness
Open source, data portability, and self-hosting stories
Postmortems learning — stories about postmortems learning in this arenaPostmortems learning
Stories about postmortems learning in this arena
Privacy posture — data-handling and privacy storiesPrivacy posture
Data-handling and privacy stories
Status communication — stories about status communication in this arenaStatus communication
Stories about status communication in this arena
Sorted by importance (agentic first) (high → low) · 54/54 stories · click a row’s chevron for the rationale and evidence
Connect an agent via an official MCP server G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 3 | full | 8/10 | Tprobed | |
Drive the product through a documented public API G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 3 | full | 8/10 | Tprobed | |
Delegate tasks to a built-in AI assistant inside the product G Agentic features | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 3 | partial | 5/10 | Cclaimed | |
Plug MCP servers into this product so it can use their tools G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 3 | none | 0/10 | ||
Get AI-generated insights and suggestions from my data inside the product G Agentic features | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | full | 8/10 | Cclaimed | |
Point an agent at llms.txt or agent-oriented docs G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | full | 8/10 | Tprobed | |
Operate the product with natural-language commands G Agentic features | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | partial | 6/10 | Tprobed | |
Set up automations that run autonomously in the background G Agentic features | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | partial | 6/10 | Cclaimed | |
Run the product headlessly / in CI for automation G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | partial | 5/10 | Tprobed | |
Subscribe to events via webhooks G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | partial | 3/10 | Cclaimed | |
Build against official SDKs G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | none | 0/10 | ||
Download a machine-readable API spec (OpenAPI or equivalent) G Api quality | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | none | 0/10 | ||
Explore an interactive API reference with runnable examples G Api quality | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | none | 0/10 | ||
Issue scoped/least-privilege API credentials for an agent G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | none | 0/10 | ||
Rely on versioned APIs with a documented deprecation policy G Api quality | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | none | 0/10 | ||
Use an official CLI G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | none | untested | none yet | |
Test against a sandbox environment without touching production data G Api quality | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 1 | none | untested | none yet | |
Alerts from my monitoring tools are ingested through documented sources and routed to the right team by conditions I define C Routing | sre | Alerting escalation — stories about alerting escalation in this arenaAlerting escalation | 3 | full | 8/10 | Cclaimed | |
Define rules that trigger actions automatically on events G | ai-native user | Automation depth — how much of the product can run unattendedAutomation depth | 3 | full | 8/10 | Cclaimed | |
Postmortems follow a real workflow — templates, drafting from the timeline, review, and publication G Postmortems | engineering leader | Postmortems learning — stories about postmortems learning in this arenaPostmortems learning | 3 | partialenterprise | 7/10 | Cclaimed | |
AI writes the incident as it happens — live summaries, drafted updates, and scribed call notes — so responders respond instead of typing C Ai summaries | ai-native user | Ai incident — stories about ai incident in this arenaAi incident | 3 | partial | 6/10 | Cclaimed | |
Build on-call schedules with rotations, layers, time zones, and round-robin coverage that match how my teams actually work C Schedules | sre | On call scheduling — stories about on call scheduling in this arenaOn call scheduling | 3 | partial | 6/10 | Cclaimed | |
Declare and run an incident from chat — Slack or Teams — with channels, roles, and updates created for me C Declaration | on-call engineer | Incident response — stories about incident response in this arenaIncident response | 3 | partial | 6/10 | Cclaimed | |
My agent can acknowledge, escalate, and resolve incidents end to end through a documented API or MCP connection — no dashboard in the loop C Agent ops | ai-native user | Ai incident — stories about ai incident in this arenaAi incident | 3 | partial | 6/10 | Tprobed | |
Escalation policies walk unacknowledged pages through multiple steps — delays, fallback responders, and repeat rounds — until someone acknowledges C Escalation | sre | Alerting escalation — stories about alerting escalation in this arenaAlerting escalation | 3 | partial | 5/10 | Cclaimed | |
Prevent my data from being used to train AI models G | ai-native user | Privacy posture — data-handling and privacy storiesPrivacy posture | 3 | partial | 3/10 | Cclaimed | |
First-party integrations cover my observability stack — Datadog, Grafana, Prometheus, CloudWatch, Sentry — with documented setup C Alert sources | sre | Integrations observability — stories about integrations observability in this arenaIntegrations observability | 3 | none | 0/10 | ||
Export all of my data in open formats and leave G | ai-native user | Openness — open source, data portability, and self-hosting storiesOpenness | 3 | none | untested | none yet | |
Self-host the core product G | ai-native user | Openness — open source, data portability, and self-hosting storiesOpenness | 3 | n/a | untested | none yet | |
AI drafts the postmortem from the incident record — timeline, contributing factors, follow-ups — ready for human review G Ai summaries | ai-native user | Ai incident — stories about ai incident in this arenaAi incident | 2 | full | 8/10 | Cclaimed | |
Runbooks attach to incidents and their steps can trigger automatically — creating channels, assigning tasks, running diagnostics C Runbooks | sre | Automation runbooks — stories about automation runbooks in this arenaAutomation runbooks | 2 | full | 8/10 | Cclaimed | |
A condition-based workflow engine automates the toil — updates, reminders, field changes — triggered by incident events C Workflows | sre | Automation runbooks — stories about automation runbooks in this arenaAutomation runbooks | 2 | full | 7/10 | Cclaimed | |
Do everything through the API that I can do in the UI G | ai-native user | Openness — open source, data portability, and self-hosting storiesOpenness | 2 | full | 7/10 | Tprobed | |
I get reliability analytics — MTTA/MTTR trends, incident load, on-call health — to see whether we are actually improving C Metrics | engineering leader | Analytics reliability — stories about analytics reliability in this arenaAnalytics reliability | 2 | full | 7/10 | Cclaimed | |
The platform integrates with the tools around the incident — Jira, Slack, Teams, Zoom, GitHub — so state flows both ways C Workflow tools | sre | Integrations observability — stories about integrations observability in this arenaIntegrations observability | 2 | partial | 7/10 | Cclaimed | |
The incident timeline is captured automatically — alerts, actions, and chat decisions — and I can edit or annotate it afterwards C Timeline | sre | Incident response — stories about incident response in this arenaIncident response | 2 | partial | 6/10 | Cclaimed | |
An AI investigator digs into the probable cause — correlating changes, telemetry, and similar past incidents — before a human even asks C Ai investigation | ai-native user | Ai incident — stories about ai incident in this arenaAi incident | 2 | partial | 5/10 | Cclaimed | |
Follow-up actions from incidents are tracked to completion and sync to our issue tracker C Follow ups | engineering leader | Postmortems learning — stories about postmortems learning in this arenaPostmortems learning | 2 | partial | 5/10 | Cclaimed | |
Publish a hosted public status page — custom domain, subscriber notifications — driven from incident state G Status pages | engineering leader | Status communication — stories about status communication in this arenaStatus communication | 2 | partial | 5/10 | Cclaimed | |
Pages reach me over the channels I choose — push, SMS, phone call, and email — with per-channel notification rules C Paging | on-call engineer | Alerting escalation — stories about alerting escalation in this arenaAlerting escalation | 2 | partial | 3/10 | Cclaimed | |
Duplicate and related alerts are deduplicated and grouped so one incident pages one human, not fifty C Noise reduction | sre | Alerting escalation — stories about alerting escalation in this arenaAlerting escalation | 2 | none | 0/10 | ||
Perform bulk operations across many items at once G | ai-native user | Automation depth — how much of the product can run unattendedAutomation depth | 2 | none | 0/10 | ||
Take an override, swap a shift, or request coverage without an admin rebuilding the schedule C Schedules | on-call engineer | On call scheduling — stories about on call scheduling in this arenaOn call scheduling | 2 | none | 0/10 | ||
A full mobile app lets me acknowledge, escalate, and resolve from my phone at 3am C Mobile | on-call engineer | Mobile experience — stories about mobile experience in this arenaMobile experience | 2 | none | untested | none yet | |
Choose where my data is stored (region/residency) G | ai-native user | Privacy posture — data-handling and privacy storiesPrivacy posture | 2 | none | untested | none yet | |
Control data retention and deletion G | ai-native user | Privacy posture — data-handling and privacy storiesPrivacy posture | 2 | none | untested | none yet | |
Opt out of telemetry and usage tracking G | ai-native user | Privacy posture — data-handling and privacy storiesPrivacy posture | 2 | none | untested | none yet | |
Read the product's source under an open license G | ai-native user | Openness — open source, data portability, and self-hosting storiesOpenness | 2 | none | untested | none yet | |
Schedule recurring jobs or workflows G | ai-native user | Automation depth — how much of the product can run unattendedAutomation depth | 2 | none | untested | none yet | |
Incidents carry defined roles (commander, comms lead) and task checklists so response stays coordinated under pressure C Coordination | engineering leader | Incident response — stories about incident response in this arenaIncident response | 1 | full | 9/10 | Cclaimed | |
Internal stakeholders get structured incident updates they can subscribe to, without joining the war room C Communications | engineering leader | Incident response — stories about incident response in this arenaIncident response | 1 | partial | 7/10 | Cclaimed | |
My shifts sync to my personal calendar via a feed so I always know when I'm on the hook C Quality of life | on-call engineer | On call scheduling — stories about on call scheduling in this arenaOn call scheduling | 1 | none | 0/10 | ||
Run private or internal status pages with access control for customer-specific or employee-only audiences G Status pages | engineering leader | Status communication — stories about status communication in this arenaStatus communication | 1 | none | 0/10 | ||
Version, review, and roll back my automations G | ai-native user | Automation depth — how much of the product can run unattendedAutomation depth | 1 | none | 0/10 |
Opportunities — the stories that would move this product's scores, from its own judged verdictsOpportunitiestop 8 of 41 stories with headroom
What would move FireHydrant’s scores — derived from its own judged verdicts, biggest headroom first. Each line quotes what the judge found missing; shipping it (or evidencing it publicly) is the fix.
Agenticness — how well agents can access and operate the productPlug MCP servers into this product so it can use their tools
nonemoves agent-readyimpact 45
Missing: any documentation of FireHydrant acting as an MCP client, configuration for adding external MCP servers, or AI features that invoke external MCP tools.
Integrations observability — stories about integrations observability in this arenaFirst-party integrations cover my observability stack — Datadog, Grafana, Prometheus, CloudWatch, Sentry — with documented setup
nonemoves PA Scoreimpact 30
Missing: named integration pages/setup docs for Datadog, Grafana, Prometheus, CloudWatch, and Sentry.
Openness — open source, data portability, and self-hosting storiesExport all of my data in open formats and leave
nonemoves PA Scoreimpact 30
There is no evidence of a bulk data export feature or open-format export/portability tool for FireHydrant; the API (docs-18,21,48) allows programmatic access but no documented full-data export/leave capability is described.
Agenticness — how well agents can access and operate the productUse an official CLI
nonemoves agent-readyimpact 30
No evidence pack item references an official FireHydrant CLI tool; only an API (docs-18/21/48), an MCP server (docs-49, probe-3), and web/Slack integrations are documented.
Agenticness — how well agents can access and operate the productIssue scoped/least-privilege API credentials for an agent
nonemoves agent-readyimpact 30
FireHydrant's docs describe API keys but explicitly state they grant Owner permissions by default with no mention of scoped or least-privilege roles for API keys or agent credentials (firehydrant-docs-33, firehydrant-docs-18, firehydrant-docs-48).
Agenticness — how well agents can access and operate the productBuild against official SDKs
nonemoves agent-readyimpact 30
Missing: any documented official SDK/library in a specific language, package registry listings, or SDK usage examples.
Agenticness — how well agents can access and operate the productExplore an interactive API reference with runnable examples
nonemoves API qualityimpact 30
FireHydrant documents a REST API and API keys but there's no evidence of an interactive API reference with runnable examples; a probe explicitly found no OpenAPI/Swagger spec exposed (all candidate paths 404), suggesting no interactive API explorer exists.
Agenticness — how well agents can access and operate the productDownload a machine-readable API spec (OpenAPI or equivalent)
nonemoves API qualityimpact 30
FireHydrant has an API reference (docs-21, docs-48) but explicit probes for standard OpenAPI spec locations (openapi.json, swagger.json, etc.) all returned 404, and no evidence of a downloadable machine-readable spec anywhere else in the pack.
Showing the top 8 of 41 — every none/partial verdict in the story verdicts table is headroom.
Think a verdict is wrong? Every verdicts-table row has a Flag link — see the methodology.
Coverage map — which docs area, API section, or community source covers which judged storiesCoverage map6 surfaces · 31 covered stories
Where the cited evidence behind each covered verdict came from — the same citations the verdicts table shows, no extra judging.
docs28 stories
- Run the product headlessly / in CI for automation
- Drive the product through a documented public API
- Get AI-generated insights and suggestions from my data inside the product
- Set up automations that run autonomously in the background
- Delegate tasks to a built-in AI assistant inside the product
- Operate the product with natural-language commands
- My agent can acknowledge, escalate, and resolve incidents end to end through a documented API or MCP connection — no dashboard in the loop
- An AI investigator digs into the probable cause — correlating changes, telemetry, and similar past incidents — before a human even asks
- AI drafts the postmortem from the incident record — timeline, contributing factors, follow-ups — ready for human review
- AI writes the incident as it happens — live summaries, drafted updates, and scribed call notes — so responders respond instead of typing
- Escalation policies walk unacknowledged pages through multiple steps — delays, fallback responders, and repeat rounds — until someone acknowledges
- Pages reach me over the channels I choose — push, SMS, phone call, and email — with per-channel notification rules
- Alerts from my monitoring tools are ingested through documented sources and routed to the right team by conditions I define
- I get reliability analytics — MTTA/MTTR trends, incident load, on-call health — to see whether we are actually improving
- Define rules that trigger actions automatically on events
- Runbooks attach to incidents and their steps can trigger automatically — creating channels, assigning tasks, running diagnostics
- A condition-based workflow engine automates the toil — updates, reminders, field changes — triggered by incident events
- Internal stakeholders get structured incident updates they can subscribe to, without joining the war room
- Incidents carry defined roles (commander, comms lead) and task checklists so response stays coordinated under pressure
- Declare and run an incident from chat — Slack or Teams — with channels, roles, and updates created for me
- The incident timeline is captured automatically — alerts, actions, and chat decisions — and I can edit or annotate it afterwards
- The platform integrates with the tools around the incident — Jira, Slack, Teams, Zoom, GitHub — so state flows both ways
- Build on-call schedules with rotations, layers, time zones, and round-robin coverage that match how my teams actually work
- Do everything through the API that I can do in the UI
- Follow-up actions from incidents are tracked to completion and sync to our issue tracker
- Postmortems follow a real workflow — templates, drafting from the timeline, review, and publication
- Prevent my data from being used to train AI models
- Publish a hosted public status page — custom domain, subscriber notifications — driven from incident state
GitHub README6 stories
- Point an agent at llms.txt or agent-oriented docs
- Run the product headlessly / in CI for automation
- Connect an agent via an official MCP server
- Drive the product through a documented public API
- Operate the product with natural-language commands
- My agent can acknowledge, escalate, and resolve incidents end to end through a documented API or MCP connection — no dashboard in the loop
API reference6 stories
- Run the product headlessly / in CI for automation
- Drive the product through a documented public API
- Subscribe to events via webhooks
- My agent can acknowledge, escalate, and resolve incidents end to end through a documented API or MCP connection — no dashboard in the loop
- The platform integrates with the tools around the incident — Jira, Slack, Teams, Zoom, GitHub — so state flows both ways
- Do everything through the API that I can do in the UI
OpenAPI spec2 stories
Probe proofs — replayable recordings from the probe harnessProbe proofs
Replayable recordings from our probe harness — see the Prove-It protocol to submit one.
$printf '<jsonrpc initialize>' | npx -y firehydrant-mcp start --transport stdio # full keyless stdio handshakereproduced$ printf '<jsonrpc initialize>' | npx -y firehydrant-mcp start --transport stdio # full [redacted]less stdio handshake
{"result":{"protocolVersion":"2025-06-18","capabilities":{"tools":{"listChanged":true},"prompts":{"listChanged":true}},"serverInfo":{"name":"FireHydrant","version":"0.0.4"}},"jsonrpc":"2.0","id":1}
Business model
Free tier for up to 10 responders (2 runbooks, 1 public status page); Pro is $25/responder/month billed annually with more runbooks, retrospectives, and unlimited webhooks; Enterprise is custom.
pricing ↗Score trend
How this product’s scores have moved as evidence and verdicts are re-derived — a point per change, not per day.
Try Experimental
Run it in the microterminal →Recorded agent sessions — and a live MCP handshake where the vendor ships one.
Flag
⚑ Flag a verdictThink a verdict is wrong? Opens a prefilled GitHub issue — or use the ⚑ next to any verdict above.
For agents
Agent surface uptime llms.txt 100% (30d, checked every 6h since Sep 8 '26)
