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 ``` . ├── .venv/ # Virtual environment (gitignored) ├── client-server/ # MCP client and server implementation │ ├── client-sse.py # SSE client │ ├── client-stdio.py # stdio client │ └── server.py # MCP server ├── gemini-llm-integration/ # Gemini LLM integration │ ├── client-simple.py # Simple Gemini client │ ├── server.py # Gemini server implementation │ └── data/ # Knowledge base and data files ├── .env # Environment variables ├── .env.example # Example environment variables ├── requirements.txt # Project dependencies └── test_gemini.py # Test script for Gemini API ``` ## 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: ```bash uv venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate ``` 3. Install dependencies: ```bash uv pip install -r requirements.txt ``` 4. Copy `.env.example` to `.env` and update with your API keys: ```bash cp .env.example .env # Edit .env with your API keys ``` ## Running the Project ### MCP Server 1. Start the MCP server: ```bash cd client-server python server.py ``` 2. In a separate terminal, run a client: ```bash # For SSE client python client-sse.py # For stdio client python client-stdio.py ``` ### Gemini Integration 1. Start the Gemini server: ```bash cd gemini-llm-integration python server.py ``` 2. Run the Gemini client: ```bash python client-simple.py ``` ## Development - Format code: ```bash black . isort . ``` - Run tests: ```bash pytest ``` - Type checking: ```bash 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