MCP with Gemini Integration

MCP Project with Gemini Integration

This project implements a Model Control Protocol (MCP) server with Google Gemini LLM integration, providing a flexible framework for building AI-powered applications.

Project Structure

Prerequisites

• Python 3.8+
• UV package manager (pip install uv)
• Google Gemini API key (for Gemini integration)

Setup

1. Clone the repository and navigate to the project directory.
2. Create and activate a virtual environment:
   uv venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate
3. Install dependencies:
   uv pip install -r requirements.txt
4. Copy .env.example to .env and update with your API keys:
   cp .env.example .env # Edit .env with your API keys

Running the Project

MCP Server

1. Start the MCP server:
   cd client-server python server.py
2. In a separate terminal, run a client:
   # For SSE client python client-sse.py # For stdio client python client-stdio.py

Gemini Integration

1. Start the Gemini server:
   cd gemini-llm-integration python server.py
2. Run the Gemini client:
   python client-simple.py

Development

• Format code:
  black . isort .
• Run tests:
  pytest
• Type checking:
  mypy .

License

[Specify your license here]

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a new Pull Request