"Celery" matching MCP servers:

Enables interaction with Celery distributed task queues through MCP tools. Supports task management, monitoring worker statistics, and controlling asynchronous job execution through natural language.

A Django-native MCP application and tool registration framework inspired by Celery, providing async tool definitions, autodiscovery, and stdio/Streamable HTTP serving built on the official MCP Python SDK.

Enables asynchronous task execution and scheduling using RabbitMQ, Celery, and Redis/SQLite. Supports immediate background task execution, scheduled tasks with ISO 8601 timestamps, task status monitoring, cancellation, and completion notifications.

Enables AI assistants to diagnose a Python backend stack — Postgres, Celery, Redis and logs — in a single tool call, returning findings, a merged timeline of events, and possible cause-effect chains. All checks are strictly read-only, so an assistant can inspect blocked queries, missing workers, queue backlogs, Redis memory pressure and log error spikes without ever modifying anything.