MCP Crew AI Server

MCP Crew AI Server is a lightweight Python-based server designed to run, manage and create CrewAI workflows. This project leverages the Model Context Protocol (MCP) to communicate with Large Language Models (LLMs) and tools such as Claude Desktop or Cursor IDE, allowing you to orchestrate multi-agent workflows with ease.

Features

• Automatic Configuration: Automatically loads agent and task configurations from two YAML files (agents.yml and tasks.yml), so you don't need to write custom code for basic setups.
• Command Line Flexibility: Pass custom paths to your configuration files via command line arguments (--agents and --tasks).
• Seamless Workflow Execution: Easily run pre-configured workflows through the MCP run_workflow tool.
• Local Development: Run the server locally in STDIO mode, making it ideal for development and testing.

Installation

1. Clone the Repository:

bash git clonehttps://github.com/adam-paterson/mcp-crew-ai.git cd mcp-crew-ai

1. Install Dependencies: Ensure you have Python 3.10+ installed, then install the required packages:

bash pip install -r requirements.txt

This will install the MCP SDK, CrewAI, PyYAML and any other dependencies.

Configuration

• agents.yml: Define your agents with roles, goals, and backstories.
• tasks.yml: Define tasks with descriptions, expected outputs, and assign them to agents.

Example agents.yml:

zookeeper:
  role: Zookeeper
  goal: Manage zoo operations
  backstory: >
    You are a seasoned zookeeper with a passion for wildlife conservation...

Example tasks.yml:

write_stories:
  description: >
    Write an engaging zoo update capturing the day's highlights.
  expected_output: 5 engaging stories
  agent_name: zookeeper

Usage

To run the server with the default configuration files located in the project directory:

mcp dev server.py

To run the server with custom configuration files, pass the paths using the --agents and --tasks options:

mcp dev server.py -- --agents /path/to/agents.yml --tasks /path/to/tasks.yml

The server will start in STDIO mode and expose the run_workflow tool, which executes your configured CrewAI workflow.

Contributing

Contributions are welcome! Please open issues or submit pull requests with improvements, bug fixes, or new features.

Licence

This project is licensed under the MIT Licence. See the LICENSE file for details.

Happy workflow orchestration!