Server data from the Official MCP Registry
Check infrastructure health, manage incidents, and run runbooks in Faultline.
Check infrastructure health, manage incidents, and run runbooks in Faultline.
Remote endpoints: streamable-http: https://mcp.fltln.io/mcp
Valid MCP server (1 strong, 1 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry. 1 finding(s) downgraded by scanner intelligence.
Endpoint verified · Open access · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
This plugin requests these system permissions. Most are normal for its category.
Remote Plugin
No local installation needed. Your AI client connects to the remote endpoint directly.
Add this to your MCP configuration to connect:
{
"mcpServers": {
"io-fltln-faultline": {
"url": "https://mcp.fltln.io/mcp"
}
}
}From the project's GitHub README.
Faultline is infrastructure monitoring and incident
management for DevOps/SRE teams. This repo documents faultline-mcp —
Faultline's remote MCP server, which lets AI agents (Claude, Claude Code,
Claude Desktop, or anything MCP-compatible) operate Faultline: check
infrastructure health, inspect and act on incidents, look up who's on call,
and run approved runbooks.
This repo is documentation only. The server is hosted by Faultline at
https://mcp.fltln.io/mcp (Streamable HTTP transport) — there's nothing to
install or run yourself.
flt_....https://mcp.fltln.io/mcp, sending the key as
either X-API-Key: flt_... or Authorization: Bearer flt_....Claude Code:
claude mcp add --transport http faultline-mcp https://mcp.fltln.io/mcp \
--header "X-API-Key: flt_..."
Clients that take raw JSON config (Claude Desktop, etc.):
{
"mcpServers": {
"faultline": {
"type": "http",
"url": "https://mcp.fltln.io/mcp",
"headers": { "X-API-Key": "flt_..." }
}
}
}
| Tool | What it does |
|---|---|
list_services | Monitor inventory with current status (optional status filter) |
get_service | One service + its 10 most recent checks (for diagnosis) |
list_incidents | Open incidents (or status: "resolved" for history) |
get_incident | Full incident record: timeline, AI summary, post-mortem |
acknowledge_incident | Acknowledge an incident — stops further escalation |
resolve_incident | Resolve with an optional note (recorded on the timeline) |
who_is_on_call | Current on-call per schedule, with shift end time |
list_anomalies | Recent learned-baseline latency anomalies (observed vs baseline, z-score, hours sustained, auto-opened incident if any) |
diagnose_incident | Recommend the next action (run runbook / escalate / resolve / wait) + candidate runbooks. Analysis only — changes nothing |
run_runbook | Execute one chosen runbook against an incident — mutates infrastructure (can restart/scale services) |
run_runbook is the only tool that changes
infrastructure. Its description instructs the calling agent to use it only
after diagnose_incident recommended it and you've explicitly confirmed.Questions or issues: support@fltln.io or the Faultline dashboard.
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