Summary
Diagnose a backed-up queue and unblock it in the same conversation.
Both halves are here: list the workers and the queue depths to find the jam, then grow the pool, attach a consumer or revoke the task that is wedging it. Worth knowing before you connect it that the same surface reaches as far as shutting a worker down.
What it is
A wrapper over the Celery Flower REST API that exposes its endpoints as MCP tools. Point it at a running Flower instance and worker pools, queues and tasks become things an assistant can inspect and act on.
What you get
- Every worker and its state in real time (list_workers)
- Pool control: grow, shrink, autoscale, restart or shut a worker down (grow_worker_pool, shrink_worker_pool, autoscale_worker_pool, restart_worker_pool, shutdown_worker)
- A queue consumer added on a running worker (add_queue_consumer)
- Task management: apply, revoke, abort, and set timeouts and rate limits
- Queue inspection, including depths and consumers
- Full coverage of the Flower REST API, not a chosen subset
Requirements
Python and a reachable Flower instance: FLOWER_URL=http://localhost:5555 uvx celery-flower-mcp. Anything the Flower API can do to your cluster these tools can do, shutdown_worker included.
Setup effort
One command — FLOWER_URL=http://localhost:5555 uvx celery-flower-mcp