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Simplified MCP client wrapper for efficient server interactions

Project description

mcpconn: The Missing Connector for AI

mcpconn is a Python library that provides a simple and efficient way to connect your applications to AI models using the Multi-purpose Cooperative Protocol (MCP). It acts as a wrapper around the mcp library, offering a streamlined client interface for seamless integration with various AI providers and transport protocols.

PyPI version License: MIT Documentation Python 3.9+ Tests

Table of Contents

✨ Features

  • Simplified Client Interface: A high-level MCPClient for easy interaction with MCP servers.
  • Multi-provider Support: Out-of-the-box support for Anthropic and OpenAI models.
  • Flexible Transports: Connect to servers using STDIO, SSE, or Streamable HTTP.
  • Built-in Guardrails: Protect your application with content filtering, PII masking, and injection detection.
  • Conversation Management: Easily manage conversation history, context, and persistence.
  • Asynchronous by Design: Built with asyncio for high-performance, non-blocking I/O.
  • Extensible: Easily add new LLM providers, transports, or guardrails.

🚀 Getting Started

Installation

pip install mcpconn

Quick Start

Here's a simple example of how to use mcpconn to connect to an MCP server and interact with an AI model:

import asyncio
from mcpconn import MCPClient

async def main():
    # Connect to a local server using STDIO
    client = MCPClient(llm_provider="anthropic")
    await client.connect("python examples/simple_server/weather_stdio.py")

    # Start a conversation
    conversation_id = client.start_conversation()
    print(f"Started conversation: {conversation_id}")

    # Send a message and get a response
    response = await client.query("Hello, world!")
    print(f"AI: {response}")

    # Disconnect from the server
    await client.disconnect()

if __name__ == "__main__":
    asyncio.run(main())

📚 Documentation

For full details on all features and the complete API reference, please visit our documentation site.

The documentation is automatically generated from the main branch and includes:

  • A full Getting Started guide.
  • In-depth tutorials and examples.
  • The complete API Reference.

🗺️ Roadmap

  • Add support for more LLM providers.
  • Implement a more comprehensive test suite.
  • Add more examples and tutorials.
  • Improve documentation and type hinting.

🤝 Contributing

Contributions are welcome! If you'd like to contribute to mcpconn, please follow these steps:

  1. Fork the repository.
  2. Create a new branch for your feature or bug fix.
  3. Make your changes and add tests.
  4. Ensure that the tests pass.
  5. Submit a pull request with a clear description of your changes.

📄 License

mcpconn is licensed under the MIT License.

⚠️ Disclaimer

This project is under active development and may undergo significant changes.

Code of Conduct

We are committed to providing a welcoming and inclusive environment for everyone. Please read and follow our Code of Conduct.

🛡️ Security

If you discover a security vulnerability, please report it to us by emailing 2796gaurav@gmail.com. We will address all reports promptly.

🌟 Showcase

Have you built something cool with mcpconn? Written an article or created a video? We'd love to see it! Please open a pull request to add your project to this list.

💬 Support

If you have questions or need help, please open an issue in the issue tracker.

🐍 Supported Python Versions

mcpconn is tested and supported on the following Python versions:

  • Python 3.9
  • Python 3.10
  • Python 3.11
  • Python 3.12

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