https://github.com/adam-paterson/mcp-crew-ai
MCP Crew AI Server is a lightweight Python-based server designed to run, manage and create CrewAI workflows.
https://github.com/adam-paterson/mcp-crew-ai
agents ai mcp mcp-server
Last synced: 7 months ago
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MCP Crew AI Server is a lightweight Python-based server designed to run, manage and create CrewAI workflows.
- Host: GitHub
- URL: https://github.com/adam-paterson/mcp-crew-ai
- Owner: adam-paterson
- Created: 2025-03-10T09:16:03.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-03-18T08:17:39.000Z (7 months ago)
- Last Synced: 2025-03-18T09:28:39.462Z (7 months ago)
- Topics: agents, ai, mcp, mcp-server
- Language: Python
- Homepage:
- Size: 150 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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!