Queue Doctor MCP server

mcp-queue-doctor

An MCP server that diagnoses Postgres job queues — pg-boss and graphile-worker. Retry storms, stuck workers, missed schedules, expiry overruns: what is broken, why, and the safest way to recover.

❌ "3 jobs in enrichment/corpus-fill are in state 'failed'."

✅ "enrichment/corpus-fill failed 140 times over 3m, peaking at 50 failures in a
   single minute. 91% share one error, which looks like an upstream rate limit.
   This is one fault reproduced many times, not many separate faults — so the fix
   belongs at the source, and retrying the jobs individually will reproduce it.

   Recovery, safest first:
     1. Stop enqueuing to this queue — every new job feeds the same failure.
     2. Confirm when the upstream quota resets; treat that as the time to resume.
     3. Add a cooldown gate after N consecutive 429s.
     ⚠ Do NOT bulk-retry yet — the upstream is still limited.

   Evidence: 140 failures, 91% 'HTTP 429 Too Many Requests (daily quota
   exceeded)', peak 50/min, busiest minutes [...], 12 other errors [...]"

The second answer is the product. Every finding carries the evidence it was drawn from, so you — or an agent — can check the reasoning instead of trusting it.

Where the heuristics come from

The rules are extracted from a morning health check that has run daily in production since April 2026 against a pg-boss instance driving ~30 cron queues. Every threshold was tuned by a real false positive or a real missed failure, and each rule below names the incident that motivated it. That provenance is the point: these are not heuristics invented for a README.

Install

npm install -g mcp-queue-doctor

{
  "mcpServers": {
    "queue-doctor": {
      "command": "mcp-queue-doctor",
      "env": {
        "QUEUE_DOCTOR_DATABASE_URL": "postgres://readonly:pw@localhost:5432/app"
      }
    }
  }
}

Then ask: "Is anything wrong with my job queue?"

Want to see it work first? examples/demo spins up a throwaway Postgres and manufactures seven failures in about a minute. It also plants a graphile-worker instance in the same database, where four of the seven rules go deliberately silent — the clearest way to see what capability declaration actually buys you.

Connecting it

The server speaks stdio, so every MCP client starts it as a subprocess. The only thing that varies is where the config lives — and whether that process can reach your database.

Claude Desktop — ~/Library/Application Support/Claude/claudedesktopconfig.json on macOS, %APPDATA%\Claude\claudedesktopconfig.json on Windows. Use the mcpServers block above, then restart the app.

Desktop launches its subprocesses from the app bundle, not a login shell, so PATH is minimal and a bare mcp-queue-doctor or npx often fails to resolve. Give it an absolute path — which mcp-queue-doctor after a global install, or the absolute path to npx with ["-y", "mcp-queue-doctor"] as its args.

Claude Code — one command, no file editing:

claude mcp add queue-doctor -e QUEUE_DOCTOR_DATABASE_URL=postgres://... -- npx -y mcp-queue-doctor

Add -s project to write a checked-in .mcp.json at the repo root instead of your personal config, so everyone working in that repo gets the tool.

Cloud / remote sessions (Claude Code on the web, and any other headless runner) — a checked-in .mcp.json is the only mechanism that works, because nobody is there to answer an approval prompt. Reference the connection string rather than committing it; Claude Code expands ${VAR} and ${VAR:-default} in .mcp.json:

{
  "mcpServers": {
    "queue-doctor": {
      "command": "npx",
      "args": ["-y", "mcp-queue-doctor"],
      "env": { "QUEUE_DOCTOR_DATABASE_URL": "${QUEUE_DOCTOR_DATABASE_URL}" }
    }
  }
}

Shortened. The full README is on GitHub.