Celery MCP server

mcp-server-celery

<!-- mcp-name: io.github.taltal-beep/mcp-server-celery -->

An MCP server that lets AI agents debug a running Celery deployment. It checks which workers are alive, what each one consumes, and where messages are piling up — the checks you'd otherwise run by hand with celery inspect and rabbitmqctl every time something gets stuck.

It is read-only by default. Broker passwords are masked in every response, including the broker info that Celery's own inspect stats sends back in plain text.

Works with RabbitMQ (amqp://) and Redis (redis://, rediss://).

Tools

Coming next: activetasks, reservedtasks, scheduledtasks, taskstatus, and diagnose_stuck, which cross-references the above to flag queues no worker listens to, tasks routed to queues with no consumer, and workers that stopped responding. See the roadmap.

Install

Requires Python 3.10+ and uv.

uvx mcp-server-celery

Configuration

All configuration is through environment variables.

CELERY_APP has to be importable, so run the server from your project's directory and environment:

uv run --with mcp-server-celery mcp-server-celery

Connect it

Claude Code

claude mcp add celery -e CELERY_BROKER_URL=redis://localhost:6379/0 -- uvx mcp-server-celery

Cursor

In .cursor/mcp.json:

{
  "mcpServers": {
    "celery": {
      "command": "uvx",
      "args": ["mcp-server-celery"],
      "env": { "CELERY_BROKER_URL": "redis://localhost:6379/0" }
    }
  }
}

Claude Desktop

In claudedesktopconfig.json, use the same command, args and env block as Cursor, under mcpServers.

Example

Ask the agent "why are my email tasks not running?" and it can call queue_depths:

{
  "broker": "amqp://app:****@rabbit:5672//",
  "queues": [
    { "queue": "celery", "exists": true, "messages": 0, "consumers": 2, "workers_consuming": ["w1@web", "w2@web"] },
    { "queue": "emails", "exists": true, "messages": 1843, "consumers": 0, "workers_consuming": [] }
  ]
}

emails has a backlog and no worker consumes it: the workers were started without -Q emails.

Notes on the data

Shortened. The full README is on GitHub.