CrewAI Workflow

by adam-paterson

8 stars

361 downloads

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GitHub

About

Lightweight Python server for dynamically loading CrewAI multi-agent workflows from YAML configurations, enabling flexible task orchestration with minimal custom code and seamless tool integration.

Details

Author: adam-paterson
Repository: adam-paterson/mcp-crew-ai
GitHub stars: 8
Downloads: 361
Categories: Productivity, Developer Tools, Design, File Management, AI, Infrastructure, Project Management, Other
Tags: #integration

- 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.

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below
Plugin Name CrewAI Workflow

Command (node, npx, python, etc.) npx
Arguments
- Argument 1 -y
- Argument 2 @highlight/mcp-server
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "crewai workflow": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "crewai workflow": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "crewai workflow": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "crewai workflow": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "crewai workflow": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "crewai workflow": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "crewai workflow": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "crewai workflow": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "crewai workflow": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "crewai workflow": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "crewai workflow": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "crewai workflow": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "crewai workflow": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

JSON

{
    "mcpServers": {
        "crewai workflow": {
            "env": {},
            "args": [
                "/c",
                "npx",
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "crewai workflow": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

From the repository

- 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:

bash pip install mcp-crew-ai

bash pip install git+https://github.com/adam-paterson/mcp-crew-ai.git

bash git clone https://github.com/adam-paterson/mcp-crew-ai.git cd mcp-crew-ai pip install -e . ```

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "crewai workflow": {
            "env": {},
            "args": [
                "-y",
                "@highlight/mcp-server"
            ],
            "command": "npx"
        }
    }
}

Linux

{
    "env": [],
    "args": [
        "-y",
        "@highlight/mcp-server"
    ],
    "command": "npx"
}

Macos

{
    "env": [],
    "args": [
        "-y",
        "@highlight/mcp-server"
    ],
    "command": "npx"
}

Windows

{
    "env": [],
    "args": [
        "/c",
        "npx",
        "-y",
        "@highlight/mcp-server"
    ],
    "command": "cmd"
}

<div align="center">
CrewAI Logo
</div>

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

Installation

There are several ways to install the MCP Crew AI server:

Option 1: Install from PyPI (Recommended)

pip install mcp-crew-ai

Option 2: Install from GitHub

pip install git+https://github.com/adam-paterson/mcp-crew-ai.git

Option 3: Clone and Install

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

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Details

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Setting up with Highlight

From the repository

Claude Desktop / Cursor

Linux

Macos

Windows

MCP Crew AI Server

Features

Installation

Option 1: Install from PyPI (Recommended)

Option 2: Install from GitHub

Option 3: Clone and Install

Requirements

Configuration

Install

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