iflow-mcp_vinnyvangogh-gemini-mcp-server 0.1.3

MCP server for Google Gemini CLI integration

Gemini Code Assist MCP

A robust Model Context Protocol (MCP) server that integrates Google Gemini CLI with Claude Code for AI-powered development assistance.

🌟 No API key required - Uses your existing Google Cloud authentication
🚀 Claude Code compatible - Works seamlessly with both Claude Code and Claude Desktop
🔍 Comprehensive analysis - Code review, bug analysis, feature planning, and code explanation
🛠️ CLI interface - Test and develop with the included CLI tool

Quick Start

1. Install and authenticate Gemini CLI:

   npm install -g @google/gemini-cli
   gcloud auth login
   

2. Clone and install the MCP server:

   git clone https://github.com/VinnyVanGogh/gemini-code-assist-mcp.git
   cd gemini-code-assist-mcp
   uv sync
   

3. Test the CLI:

   uv run gemini-mcp-cli --show-prompts review file --file your_code.py
   

4. Set up in Claude Code (see Claude Code Setup below)

Features

This MCP server provides the following tools for development assistance:

🔍 Code Analysis & Review

• gemini_review_code: Comprehensive code review with quality, security, and performance analysis
• gemini_analyze_security: Security-focused code analysis

📋 Feature Planning & Documentation

• gemini_proofread_feature_plan: Review and improve feature specifications
• gemini_suggest_implementation: Implementation guidance and architecture suggestions

🐛 Bug Analysis & Debugging

• gemini_analyze_bug: Root cause analysis and fix suggestions
• gemini_debug_assistance: Debugging workflow assistance

📖 Code Understanding

• gemini_explain_code: Clear code explanations with varying detail levels
• gemini_generate_tests: AI-assisted test generation

Prerequisites

Before using this MCP server, ensure you have:

1. Gemini CLI installed and configured

   # Install Gemini CLI (follow Google's official instructions)
   # https://github.com/google-gemini/gemini-cli
   

2. Google authentication set up

   # Authenticate with Google (no API key needed)
   gcloud auth login
   

3. Python 3.11+ with UV package manager

   # Install UV if not already installed
   curl -LsSf https://astral.sh/uv/install.sh | sh
   

Installation

1. Clone and install the MCP server:

   git clone <repository-url>
   cd gemini-mcp-server
   uv sync
   

2. Test the installation:

   # Test Gemini CLI access
   gemini --help
   
   # Test the MCP server
   uv run python src/main.py
   

3. Test the installation:

   # Run installation tests
   uv run python test_installation.py
   

4. Install in Claude Code:

   # Install the server for Claude Code (recommended)
   uv run mcp install src/main.py
   
   # Or with a custom name
   uv run mcp install src/main.py --name "Gemini Assistant"
   
   # Alternative: Manual installation with JSON
   claude mcp add '{
     "name": "Gemini Code Assist",
     "command": "uv",
     "args": ["run", "python", "src/main.py"],
     "cwd": "/Users/your-username/path/to/gemini-code-assist-mcp",
     "env": {
       "GEMINI_DEBUG": "false"
     }
   }'
   

Claude Code Setup

Method 1: Local MCP Server (Recommended)

1. Install the MCP server:

   git clone https://github.com/VinnyVanGogh/gemini-code-assist-mcp.git
   cd gemini-code-assist-mcp
   uv sync
   

2. Configure Claude Code:

   • Open Claude Code
   • Navigate to Settings > MCP Servers
   • Add a new server with these settings:
     • Name: Gemini Code Assist
     • Command: uv
     • Args: ["run", "python", "src/main.py"]
     • Working Directory: /path/to/gemini-code-assist-mcp

3. Alternative: Edit configuration file:

   # Edit your Claude Code configuration
   code ~/.claude/settings.json
   

   Add this configuration:

   {
     "mcp": {
       "servers": {
         "gemini-code-assist": {
           "command": "uv",
           "args": ["run", "python", "src/main.py"],
           "cwd": "/path/to/gemini-code-assist-mcp",
           "env": {
             "GEMINI_DEBUG": "false"
           }
         }
       }
     }
   }
   

Method 2: Claude Desktop Configuration

For Claude Desktop users, edit claude_desktop_config.json:

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

{
  "mcpServers": {
    "gemini-code-assist": {
      "command": "uv",
      "args": [
        "run",
        "python",
        "/path/to/gemini-code-assist-mcp/src/main.py"
      ],
      "env": {
        "GEMINI_DEBUG": "false",
        "GEMINI_SANDBOX": "false"
      }
    }
  }
}

Method 3: Remote MCP Server

For remote deployment, you can host the MCP server and connect via URL:

{
  "mcpServers": {
    "gemini-code-assist": {
      "url": "https://your-deployment-url.com/mcp",
      "auth": {
        "type": "bearer",
        "token": "your-auth-token"
      }
    }
  }
}

Verification

After configuration, restart Claude Code/Desktop and verify the server is loaded:

1. Check the MCP servers list in settings
2. Look for "Gemini Code Assist" in available tools
3. Try using a tool to confirm it works

Usage in Claude Code

Once installed, you can use the Gemini tools directly in Claude Code:

Code Review Example

@gemini_review_code
{
  "code": "def factorial(n):\n    if n <= 1:\n        return 1\n    return n * factorial(n-1)",
  "language": "python",
  "focus": "performance"
}

Feature Plan Review Example

@gemini_proofread_feature_plan
{
  "feature_plan": "Add user authentication with OAuth2...",
  "context": "Web application with React frontend",
  "focus_areas": "security,usability,implementation"
}

Bug Analysis Example

@gemini_analyze_bug
{
  "bug_description": "Application crashes when processing large files",
  "code_context": "def process_file(filepath):\n    with open(filepath) as f:\n        return f.read()",
  "error_logs": "MemoryError: Unable to allocate array",
  "environment": "Python 3.11, 8GB RAM"
}

Configuration

Server Configuration

The server can be configured by modifying the ServerConfig in src/core/config.py:

config = ServerConfig(
    name="Custom Gemini Server",
    gemini_options=GeminiOptions(
        model="gemini-2.5-pro",  # or "gemini-pro"
        sandbox=False,           # Enable sandbox mode
        debug=False             # Enable debug logging
    ),
    enable_caching=True,
    max_file_size_mb=10.0
)

Gemini CLI Options

The server supports all major Gemini CLI options:

• model: Choose the Gemini model (gemini-2.5-pro, gemini-pro)
• sandbox: Run in sandbox mode for code execution
• debug: Enable detailed logging
• all_files: Include all files in context
• yolo: Auto-accept all actions
• checkpointing: Enable file edit checkpointing

Custom Templates

You can add custom prompt templates by extending the ConfigManager:

from src.core.config import ConfigManager, PromptTemplate

manager = ConfigManager()
custom_template = PromptTemplate(
    name="custom_review",
    description="Custom code review template",
    system_prompt="You are a specialized reviewer for...",
    user_template="Review this {language} code: {code}",
    variables={"language": "Programming language", "code": "Code to review"}
)
manager.add_template(custom_template)

Available Resources

The server exposes several MCP resources for inspection:

• gemini://config: Current server configuration
• gemini://templates: Available prompt templates
• gemini://status: Gemini CLI status and authentication info

Access these in Claude Code:

Can you show me the current Gemini configuration?
# Claude will automatically fetch gemini://config resource

CLI Usage

The included CLI tool allows you to test and use Gemini functionality directly:

Basic Commands

# Check version
uv run gemini-mcp-cli version

# Show help
uv run gemini-mcp-cli --help

# Check status and authentication
uv run gemini-mcp-cli status check

Code Review

# Review a Python file
uv run gemini-mcp-cli review file --file code.py --focus security

# Review with input/output transparency
uv run gemini-mcp-cli --show-prompts review file --file code.py

# Review from stdin
cat code.py | uv run gemini-mcp-cli review stdin --focus performance

# Output as JSON
uv run gemini-mcp-cli --json review file --file code.py

Feature Planning

# Review a feature plan
uv run gemini-mcp-cli feature review --file feature-plan.md

# Interactive feature planning
uv run gemini-mcp-cli feature interactive

Bug Analysis

# Analyze a bug with context
uv run gemini-mcp-cli bug analyze --description "App crashes on startup" --code-file main.py

# Interactive bug analysis
uv run gemini-mcp-cli bug interactive

Code Explanation

# Explain code at different levels
uv run gemini-mcp-cli explain file --file complex.py --level beginner
uv run gemini-mcp-cli explain file --file algorithm.py --level advanced

Global Options

• --show-prompts: Show input prompts and raw responses for transparency
• --json: Output results in JSON format
• --verbose: Enable verbose output
• --debug: Enable debug mode
• --no-color: Disable colored output
• --model: Specify Gemini model (default: gemini-2.5-pro)
• --sandbox: Enable sandbox mode

Development

Running Tests

# Run all tests
uv run pytest

# Run specific test files
uv run pytest src/core/tests/test_gemini_client.py -v

# Run with coverage
uv run pytest --cov=src --cov-report=html

Code Quality

# Format code
uv run ruff format .

# Run linter
uv run ruff check .

# Run type checker
uv run mypy .

Development Mode

For development and testing:

# Run server in development mode
uv run mcp dev src/main.py

# This allows testing with MCP Inspector
# Visit the provided URL to test tools interactively

Architecture

The server follows a vertical slice architecture:

src/
├── main.py                 # Entry point
├── server/                 # FastMCP server implementation
│   ├── gemini_server.py   # Main server with tools
│   └── tests/
├── core/                   # Core utilities
│   ├── gemini_client.py   # Gemini CLI wrapper
│   ├── config.py          # Configuration management
│   └── tests/
├── features/               # Feature modules
│   ├── proofreading/      # Review and proofreading
│   ├── analysis/          # Bug and code analysis
│   ├── utilities/         # Helper utilities
│   └── tests/
└── templates/              # Prompt templates

Key Components

1. GeminiCLIClient: Handles subprocess calls to Gemini CLI with proper authentication
2. ConfigManager: Manages server configuration and prompt templates
3. FastMCP Server: Implements MCP tools and resources
4. Prompt Templates: Structured prompts for different use cases

Troubleshooting

Common Issues

1. "Gemini CLI not found"

   # Install Gemini CLI
   npm install -g @google/gemini-cli
   
   # Verify installation
   which gemini
   gemini --help
   

2. Authentication errors

   # Authenticate with Google Cloud
   gcloud auth login
   
   # Test authentication
   gemini -p "Hello world"
   

3. "Command failed with exit code 1"

   • Check your Google authentication status: gcloud auth list
   • Verify you have access to Gemini models
   • Check internet connectivity
   • Try with --debug flag: uv run gemini-mcp-cli --debug status check

4. Tool timeouts

   • Large code files may take time to process
   • Consider breaking down large requests
   • Check if you've hit rate limits (60 requests/minute, 1000/day)

5. MCP Server not appearing in Claude

   • Verify configuration file syntax with a JSON validator
   • Check that file paths are absolute, not relative
   • Restart Claude Code/Desktop after configuration changes
   • Check Claude's MCP server logs for errors

6. "No such option: --show-prompts"

   • Make sure you're using the latest version: uv run gemini-mcp-cli version
   • The option must come before the command: --show-prompts review file not review file --show-prompts

7. MCP Server "Not connected" or timeout errors

   • Restart Claude Code after adding the MCP server
   • Verify the server is running: uv run python src/main.py (should start without errors)
   • Check that the working directory path is absolute and correct
   • Verify Google authentication: gemini -p "test"
   • Try removing and re-adding the MCP server configuration

8. "No server object found" during MCP install

   • This was fixed in v0.1.3 - make sure you're using the latest version
   • If still occurring, try the manual installation method above

Debug Mode

Enable debug mode for detailed logging:

# CLI debug mode
uv run gemini-mcp-cli --debug status check

# Environment variable
export GEMINI_DEBUG=true

Getting Help

1. Check the documentation in this README
2. Use the CLI help: uv run gemini-mcp-cli --help
3. Check existing issues on GitHub
4. Create a new issue with:
   • Your operating system and Python version
   • Complete error messages
   • Steps to reproduce the problem
   • Output of uv run gemini-mcp-cli status check

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes following the code style guidelines
4. Add tests for new functionality
5. Run the test suite and ensure all tests pass
6. Submit a pull request

Code Style

• Follow PEP 8
• Use type hints for all functions
• Add docstrings for all public functions and classes
• Keep functions under 50 lines
• Keep classes under 50 lines
• Use Pydantic models for data validation

License

MIT License - see LICENSE file for details.

Support

For issues and questions:

1. Check the troubleshooting section above
2. Search existing issues in the repository
3. Create a new issue with detailed information about your problem

Acknowledgments

• Google Gemini team for the excellent CLI tool
• Anthropic for the Model Context Protocol
• The open-source community for various dependencies