# 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)](https://modelcontextprotocol.io/introduction) 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 There are several ways to install the MCP Crew AI server: ### Option 1: Install from PyPI (Recommended) ```bash pip install mcp-crew-ai ``` ### Option 2: Install from GitHub ```bash pip install git+https://github.com/adam-paterson/mcp-crew-ai.git ``` ### Option 3: Clone and Install ```bash git clone https://github.com/adam-paterson/mcp-crew-ai.git cd mcp-crew-ai pip install -e . ``` ### Requirements - Python 3.11+ - MCP SDK - CrewAI - PyYAML ## 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`:** ```yaml zookeeper: role: Zookeeper goal: Manage zoo operations backstory: > You are a seasoned zookeeper with a passion for wildlife conservation... ``` **Example `tasks.yml`:** ```yaml write_stories: description: > Write an engaging zoo update capturing the day's highlights. expected_output: 5 engaging stories agent: zookeeper output_file: zoo_report.md ``` ## Usage Once installed, you can run the MCP CrewAI server using either of these methods: ### Standard Python Command ```bash mcp-crew-ai --agents path/to/agents.yml --tasks path/to/tasks.yml ``` ### Using UV Execution (uvx) For a more streamlined experience, you can use the UV execution command: ```bash uvx mcp-crew-ai --agents path/to/agents.yml --tasks path/to/tasks.yml ``` Or run just the server directly: ```bash uvx mcp-crew-ai-server ``` This will start the server using default configuration from environment variables. ### Command Line Options - `--agents`: Path to the agents YAML file (required) - `--tasks`: Path to the tasks YAML file (required) - `--topic`: The main topic for the crew to work on (default: "Artificial Intelligence") - `--process`: Process type to use (choices: "sequential" or "hierarchical", default: "sequential") - `--verbose`: Enable verbose output - `--variables`: JSON string or path to JSON file with additional variables to replace in YAML files - `--version`: Show version information and exit ### Advanced Usage You can also provide additional variables to be used in your YAML templates: ```bash mcp-crew-ai --agents examples/agents.yml --tasks examples/tasks.yml --topic "Machine Learning" --variables '{"year": 2025, "focus": "deep learning"}' ``` These variables will replace placeholders in your YAML files. For example, `{topic}` will be replaced with "Machine Learning" and `{year}` with "2025". ## 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!