Provides tools for managing distributed task queues, including sending tasks, monitoring task status, retrieving worker statistics, and controlling task execution (revoke/cancel tasks).
Celery MCP
A Python library that provides a connector to use Celery distributed task queues over the Model Context Protocol (MCP).
Features
- Seamless integration of Celery with MCP
- Easy-to-use API for task management
- Support for asynchronous task execution
- MCP server with tools for LLM interaction
- Comprehensive documentation and examples
Installation
Install from PyPI:
Or install from source:
Quick Start
Using the Python API
Using the MCP Server
The package includes an MCP server that exposes Celery functionality as tools that can be used by LLMs:
Available MCP Tools
- initialize_celery_connection - Initialize connection to Celery broker
- list_registered_tasks - List all registered task names
- send_task - Send a task to the Celery queue
- get_task_status - Get the status of a Celery task
- get_active_tasks - Get information about active (running) tasks
- get_scheduled_tasks - Get information about scheduled tasks
- revoke_task - Revoke (cancel) a task
- get_worker_stats - Get statistics about Celery workers
MCP Client Configuration
To use the MCP server with Claude Desktop, add this to your claude_desktop_config.json:
Documentation
Full documentation is available at https://celery-mcp.readthedocs.io/.
Contributing
We welcome contributions! Please see our Contributing Guide for details.
License
This project is licensed under the MIT License - see the LICENSE file for details.
Support
If you have any questions or issues, please open an issue on GitHub.
This server cannot be installed
hybrid server
The server is able to function both locally and remotely, depending on the configuration or use case.
Enables interaction with Celery distributed task queues through MCP tools. Supports task management, monitoring worker statistics, and controlling asynchronous job execution through natural language.