Gemini Agent MCP Server

Gemini Agent MCP Server

Provides a Model Context Protocol interface to the Gemini CLI, enabling AI agents to call the Gemini model and interact with development tools like code linting, GitHub operations, and documentation generation. Includes security measures to prevent unauthorized file access through path validation.

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README

Gemini Agent MCP Server

This server provides a Model Context Protocol (MCP) interface to the Gemini CLI. It allows AI agents and other systems to call the Gemini model and other tools by interacting with a standardized JSON-RPC endpoint.

Setup

  1. Install Dependencies:

    pip install -r requirements.txt
    
  2. Install Linters (for lint_code tool):

    • For Python linting, pylint is installed with the requirements.
    • For JavaScript linting, you need to have eslint installed and available in your system's PATH. You can typically install it using npm:
      npm install -g eslint
      
  3. Set Environment Variables:

    • Ensure you are logged in to Gemini CLI: Make sure you have the Gemini CLI installed and you are logged in. The server uses the Gemini CLI to call the Gemini model.

    • GitHub Token: For the create_github_issue and create_github_pr tools, you need to set the GITHUB_TOKEN environment variable to your GitHub personal access token.

      export GITHUB_TOKEN="YOUR_GITHUB_TOKEN"
      
    • Server Port (Optional): The server port can be configured using the MCP_PORT environment variable. It defaults to 5001.

      export MCP_PORT=8080
      
  4. Run the Server:

    python src/main.py
    

    The server will start on the port specified by MCP_PORT, or 5001 by default.

Usage

The server exposes two main endpoints:

  • GET /mcp: Returns the MCP manifest, which describes the available tools.
  • POST /jsonrpc: The JSON-RPC endpoint for calling the tools.

Example: Calling a tool using JSON-RPC

You can use curl to send a JSON-RPC request to any of the available tools. Here is an example of calling the call_gemini tool:

curl -X POST \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "method": "call_gemini",
    "params": {
      "prompt": "What is the capital of France?",
      "context": "This is a simple geography question."
    },
    "id": 1
  }' \
  http://127.0.0.1:5001/jsonrpc

This will return a JSON-RPC response with the answer from the Gemini model.

Browsable API

For development and testing, you can access a web browsable API for the JSON-RPC endpoint by navigating to http://127.0.0.1:5001/jsonrpc in your web browser.

Security

This server includes security measures to prevent unauthorized file access.

File Path Validation

All tools that accept a file path (summarize_docs, lint_code, analyze_dependencies, generate_unit_tests, generate_docstrings) perform a validation to ensure that the requested path is within the project directory. This is to prevent directory traversal attacks where a user could potentially access sensitive files outside of the project's scope.

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