About CrewAI MCP Adapter
Python library extending CrewAI's adapter ecosystem with MCP integration support and comprehensive tooling for custom agent and tool development. Features type-safe implementation with Pydantic and async/await support.
Key features
- 01Native CrewAI integration and adapter patterns
- 02MCP protocol support for tool integration
- 03Type-safe implementation with Pydantic
- 04Async/await support
- 05Detailed execution metadata
Use cases
- 01Integrate MCP services into CrewAI
- 02Add MCP tools to agents
- 03Create custom adapters to extend CrewAI
MCP support
CrewAI adapter library enabling agents to access and use tools from MCP services through MCP integration.
Details
From the project READMECrewAI MCP Adapter
A Python library extending CrewAI's adapter ecosystem with Model Context Protocol (MCP) integration support and comprehensive tooling for custom agent and tool development.
Features
- 🔌 Native CrewAI integration and adapter patterns
- 🛠️ MCP protocol support for tool integration
- 🧩 Easy-to-use interface for extending and creating new adapters
- 📝 Type-safe implementation with Pydantic
- 🔍 JSON Schema validation for tool parameters
- 🚀 Async/await support
- 📊 Detailed execution metadata
Installation
You can install the package directly from PyPI:
pip install crewai-adapters
Or install from source:
pip install git+https://github.com/dshivendra/crewai_mcp_adapter.git
Quick Start
from crewai import Agent, Task
from crewai_adapters import CrewAIAdapterClient
from crewai_adapters.types import AdapterConfig
async def main():
async with CrewAIAdapterClient() as client:
# Connect to MCP server
await client.connect_to_mcp_server(
"math",
command="python",
args=["math_server.py"]
)
# Create agent with tools
agent = Agent(
name="Calculator",
goal="Perform calculations",
tools=client.get_tools()
)
# Execute task
task = Task(
description="Calculate (3 + 5) × 12",
agent=agent
)
result = await task.execute()
print(f"Result: {result}")
if __name__ == "__main__":
import asyncio
asyncio.run(main())
Documentation
For detailed documentation, see:
Development
Prerequisites
- Python 3.11 or higher
crewaipackagepydanticpackagemcppackage
Install Development Dependencies
pip install -e ".[test,docs]"
Running Tests
pytest tests/ -v
Publishing
To publish a new version to PyPI:
- Update version in pyproject.toml
- Build the package:
python -m build - Upload to PyPI:
python -m twine upload dist/*
License
This project is licensed under the MIT License - see the LICENSE file for details.
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.
