Gemini CLI MCP Server

A Model Context Protocol (MCP) server that wraps the Gemini CLI, allowing AI assistants like Claude to use Gemini for research and analysis tasks.

Disclaimer: This is an unofficial, community-developed tool that wraps the Google Gemini CLI. It is not affiliated with, endorsed by, or sponsored by Google. All Google and Gemini trademarks, service marks, and logos are the property of Google LLC. This project is licensed under the MIT License.

Important: This tool requires that you have the Gemini CLI installed and authenticated on your system. See the Prerequisites section for details.

Features

• gemini_prompt: Send prompts to Gemini and get responses
• gemini_research: Research topics with optional file context
• gemini_analyze_code: Analyze code files for reviews, explanations, optimizations, security, or testing
• gemini_summarize: Summarize content from text or files

Installation

Quick Start (Recommended)

# Install from GitHub (latest)
uvx --from git+https://github.com/DiversioTeam/gemini-cli-mcp gemini-mcp

# Or from PyPI (when published)
uvx gemini-mcp

Development Installation

git clone https://github.com/DiversioTeam/gemini-cli-mcp
cd gemini-cli-mcp
uv sync
uv run gemini-mcp

Prerequisites

1. Python 3.10-3.13

Ensure you have Python 3.10, 3.11, 3.12, or 3.13 installed. The project is tested on all these versions.

2. Gemini CLI Installation and Authentication

This MCP server requires the Gemini CLI to be installed and authenticated:

1. Install Gemini CLI: Follow the official installation instructions for your platform
2. Authenticate: Run gemini auth login and follow the prompts
3. Verify: Test that it works by running gemini -p "Hello, world!"

If you see an authentication error, make sure you've completed the login process.

3. MCP-compatible Client

You'll need an MCP-compatible client such as:

• Claude Desktop
• Other MCP-compatible AI assistants

Usage

First Time Setup

Before running the server, verify your setup:

# Check if everything is configured correctly
gemini-mcp setup

This will check:

• Gemini CLI installation
• Authentication status
• Environment configuration

If you see authentication errors, run:

gemini auth login

Running the Server

# Method 1: Run from current directory (for development)
uv run gemini-mcp

# Method 2: Run using uvx from GitHub
uvx --from git+https://github.com/DiversioTeam/gemini-cli-mcp gemini-mcp

# Method 3: Run if installed globally
gemini-mcp

# With debug logging
LOG_LEVEL=DEBUG gemini-mcp

Configuring with Claude Code (CLI)

Method 1: Using the CLI (Recommended)

# Install from GitHub (use -- separator for proper argument parsing)
claude mcp add gemini uvx -- --from git+https://github.com/DiversioTeam/gemini-cli-mcp.git gemini-mcp

# Or install from PyPI when published
claude mcp add gemini uvx -- gemini-mcp

Method 2: Manual Configuration

Add this to ~/.config/claude-code/mcp-settings.json:

{
  "servers": {
    "gemini": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/DiversioTeam/gemini-cli-mcp.git", "gemini-mcp"]
    }
  }
}

Configuring with Claude Desktop

Add this to your Claude Desktop configuration:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json Linux: ~/.config/claude/claude_desktop_config.json

{
  "mcpServers": {
    "gemini": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/DiversioTeam/gemini-cli-mcp.git", "gemini-mcp"],
      "env": {
        "LOG_LEVEL": "INFO"
      }
    }
  }
}

Available Tools

gemini_prompt

Send a simple prompt to Gemini.

Parameters:

• prompt (required): The prompt to send
• model (optional): The Gemini model to use (default: gemini-2.5-pro)
• context (optional): Additional context to prepend to the prompt

gemini_research

Research a topic with optional file context.

Parameters:

• topic (required): The research topic or question
• files (optional): List of file paths to include as context
• model (optional): The Gemini model to use

gemini_analyze_code

Analyze code files for various purposes.

Parameters:

• files (required): List of code files to analyze
• analysis_type (required): Type of analysis - one of:
  • review: Code review for issues and improvements
  • explain: Detailed explanation of the code
  • optimize: Performance and maintainability suggestions
  • security: Security vulnerability analysis
  • test: Test case suggestions
• specific_question (optional): Additional specific question
• model (optional): The Gemini model to use

gemini_summarize

Summarize content from text or files.

Parameters:

• content (optional): Text content to summarize
• files (optional): Files to summarize (alternative to content)
• summary_type (optional): Type of summary - one of:
  • brief: 2-3 sentence summary (default)
  • detailed: Comprehensive summary
  • bullet_points: Bullet point format
  • executive: Executive summary for decision makers
• model (optional): The Gemini model to use

Note: Either content or files must be provided.

Security

This MCP server implements several security measures:

File Access Control

• Sandboxed file access: By default, file access is restricted to the current working directory
• Path validation: All file paths are validated to prevent directory traversal attacks
• Configurable allowed directories: You can specify allowed directories via:
  • Constructor parameter: GeminiMCPServer(allowed_directories=["/path/to/safe/dir"])
  • Environment variable: GEMINI_MCP_ALLOWED_DIRS=/path1:/path2:/path3

Input Sanitization

• All user inputs are properly sanitized before being passed to the CLI
• Command injection is prevented by using subprocess with argument lists (never shell=True)
• File paths are resolved and validated before use

Best Practices

• Always run the server with minimal required permissions
• Regularly update both the MCP server and Gemini CLI
• Monitor logs for any suspicious activity

Development

Setting up the Development Environment

1. Clone the repository:

   git clone https://github.com/DiversioTeam/gemini-cli-mcp.git
   cd gemini-cli-mcp
   

2. Install development dependencies:

   uv sync --all-extras --dev
   

3. Install pre-commit hooks:

   uv run pre-commit install
   

Local Testing with Claude Code

You can add the server for local testing with Claude Code CLI:

# Method 1: Add from local directory (for development)
claude mcp add gemini-local -- uv run gemini-mcp

# Method 2: Add from PyPI (when published)
claude mcp add gemini uvx -- gemini-mcp

# Method 3: Add from GitHub (development version)
claude mcp add gemini uvx -- --from git+https://github.com/DiversioTeam/gemini-cli-mcp.git gemini-mcp

# Then you can test the functionality immediately
# Example: Send prompts, research topics, analyze code, etc.

Running Tests

# Run all tests with coverage
uv run pytest

# Run specific test file
uv run pytest tests/test_tools.py

# Run with verbose output
uv run pytest -v

Code Quality

# Run linting
uv run ruff check .

# Run formatting
uv run ruff format .

# Run type checking
uv run mypy src/

# Run security checks
uv run ruff check --select S .

Troubleshooting

Quick Diagnostics

Run the setup check to diagnose common issues:

gemini-mcp setup

Common Issues

1. "Gemini CLI not found in PATH"

   • Ensure Gemini CLI is installed: which gemini
   • Add Gemini to your PATH if needed
   • Follow the installation guide for your platform

2. Authentication errors

   • The server will detect if you're not authenticated
   • Run gemini auth login to authenticate
   • Check if your credentials have expired
   • The error message will guide you on what to do

3. File access errors

   • Check that files are within allowed directories
   • Use absolute paths or ensure relative paths are correct
   • Set GEMINI_MCP_ALLOWED_DIRS for custom directories

4. "Command not found" errors

   • Run gemini-mcp setup to check your installation
   • Ensure you've installed the package: uv pip install -e .

Debug Logging

Enable debug logging to see detailed information:

LOG_LEVEL=DEBUG gemini-mcp

Contributing

We welcome contributions! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes and add tests
4. Ensure all tests pass and code quality checks succeed
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Built on top of the Model Context Protocol
• Integrates with Google's Gemini CLI (unofficial integration)
• Inspired by the MCP ecosystem and community