MCP Crew AI Server

What is MCP Crew AI Server? MCP Crew AI Server is a lightweight Python-based server designed to run, manage, and create CrewAI workflows, leveraging the Model Context Protocol (MCP) to communicate with Large Language Models (LLMs) and tools like Claude Desktop or Cursor IDE. How to use MCP Crew AI Server? To use the server, clone the repository, install the required dependencies, and run the server with default or custom configuration files using command line arguments. Key features of MCP Crew AI Server? Automatic configuration loading from YAML files for agents and tasks. Command line flexibility to specify custom paths for configuration files. Seamless execution of pre-configured workflows through the MCP run_workflow tool. Local development capability in STDIO mode for testing and development. Use cases of MCP Crew AI Server? Orchestrating multi-agent workflows for various applications. Automating tasks in a development environment. Managing complex workflows involving multiple agents and tasks. FAQ from MCP Crew AI Server? Can I customize the agents and tasks? Yes! You can define your agents and tasks in the agents.yml and tasks.yml files respectively. Is there a specific Python version required? Yes, Python 3.10 or higher is required to run this server. How do I contribute to the project? Contributions are welcome! You can open issues or submit pull requests on the GitHub repository.

As an MCP (Model Context Protocol) server, MCP Crew AI Server enables AI agents to communicate effectively through standardized interfaces. The Model Context Protocol simplifies integration between different AI models and agent systems.

To use the server, clone the repository, install the required dependencies, and run the server with default or custom configuration files using command line arguments. Key features of MCP Crew AI Server? Automatic configuration loading from YAML files for agents and tasks. Command line flexibility to specify custom paths for configuration files. Seamless execution of pre-configured workflows through the MCP run_workflow tool. Local development capability in STDIO mode for testing and development. Use cases of MCP Crew AI Server? Orchestrating multi-agent workflows for various applications. Automating tasks in a development environment. Managing complex workflows involving multiple agents and tasks. FAQ from MCP Crew AI Server? Can I customize the agents and tasks? Yes! You can define your agents and tasks in the agents.yml and tasks.yml files respectively. Is there a specific Python version required? Yes, Python 3.10 or higher is required to run this server. How do I contribute to the project? Contributions are welcome! You can open issues or submit pull requests on the GitHub repository.

Learn how to integrate this MCP server with your AI agents and leverage the Model Context Protocol for enhanced capabilities.