mcp-server-celery
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| CELERY_APP | No | Import path of your app, e.g. proj.celery:app. Lets the server see your task_queues and routes. CELERY_BROKER_URL still overrides the app's broker if both are set. | |
| CELERY_BROKER_URL | No | Broker to inspect. | amqp://guest:guest@localhost:5672// |
| CELERY_RESULT_BACKEND | No | Result backend (used by task_status, coming in the next release). | |
| CELERY_INSPECT_TIMEOUT | No | Seconds to wait for worker replies. Raise it for large or slow clusters. | 1.0 |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| logging | {} |
| prompts | {
"listChanged": true
} |
| resources | {
"subscribe": false,
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_workersA | List workers that reply to broadcast, with pool, concurrency, uptime and the queues each consumes. |
| queue_depthsB | Count waiting messages per queue and show which workers consume each one. Works on RabbitMQ and Redis. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
The two tools target distinct resources: list_workers is worker-centric while queue_depths is queue-centric. There is minor overlap since both surface the worker-to-queue consumption mapping, but the primary purpose of each is clear.
Both names are snake_case, which is consistent. However list_workers follows a verb_noun pattern while queue_depths is noun_noun, a minor deviation in convention.
Two tools is thin for a Celery monitoring server, which typically covers tasks, queues, and workers. It is not extreme, but the surface feels under-scoped for the domain.
Celery monitoring spans workers, queues, and tasks, but only worker and queue visibility is offered. Task inspection (list tasks, task status, revoke) and queue management (purge) are notably absent, leaving significant gaps.