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

Related MCP server: MCP Gemini CLI

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