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Gemini MCP Server

by bobvasic
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JavaScript
MIT License
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🤖 Gemini MCP Server

Customized for Warp Terminal

Model Context Protocol Server for Google Gemini API

Optimized for Modern Terminal Workflows

License: MIT Node.js MCP Gemini

Seamlessly integrate Google Gemini AI into your Warp terminal workflow

Features • Installation • Configuration • Usage • API Reference • Contributing

📋 Overview

Gemini MCP Server is a Model Context Protocol implementation that brings Google Gemini's powerful AI capabilities directly into your Warp terminal. Built with enterprise-grade standards, this server enables conversational AI, multi-turn dialogues, and intelligent code analysis through simple, well-defined tools.

Why Use This?

✨ Zero Configuration - Works out of the box with Warp terminal
🔒 Secure by Default - API keys stored in environment variables
⚡ High Performance - Optimized for rapid response times
🎯 Purpose-Built Tools - Three focused tools for maximum utility
🌐 Open Source - MIT licensed, community-driven development

✨ Features

💬 Single-Turn Chat

gemini_chat

Quick, stateless conversations with Gemini. Perfect for one-off questions, code generation, or content creation.

🔄 Multi-Turn Conversations

gemini_chat_with_history

Maintain context across multiple exchanges. Build complex dialogues and iterative problem-solving sessions.

🔍 Code Analysis

gemini_analyze_code

Deep code review, bug detection, optimization suggestions, and explanations. Supports multiple programming languages.

🚀 Installation

Prerequisites

  • Node.js ≥ 18.0.0

  • npm ≥ 9.0.0

  • Warp Terminal (latest version)

  • Google Gemini API Key (Get one here)

Quick Start

# Clone or download the repository git clone https://github.com/bobvasic/gemini-mcp-server.git cd gemini-mcp-server # Install dependencies npm install # Run automated setup (recommended) ./setup.sh YOUR_GEMINI_API_KEY

Manual Installation

# Install dependencies npm install # Make scripts executable chmod +x index.js setup.sh

⚙️ Configuration

Method 1: Automated Setup (Recommended)

./setup.sh YOUR_GEMINI_API_KEY

This script automatically:

  • Creates Warp MCP configuration

  • Sets up your API key securely

  • Validates the installation

Method 2: Manual Configuration

Step 1: Get Your API Key

  1. Visit Google AI Studio

  2. Sign in with your Google account

  3. Generate a new API key

  4. Copy the key (keep it secure!)

Step 2: Configure Warp

Create or edit ~/.config/warp/mcp.json:

{ "mcpServers": { "gemini": { "command": "node", "args": ["${HOME}/gemini-mcp-server/index.js"], "env": { "GEMINI_API_KEY": "your-actual-api-key-here" } } } }

⚠️ Security Best Practice: Never commit your API key to version control. Use environment variables for production deployments.

Step 3: Restart Warp

Completely quit and restart Warp terminal for changes to take effect.

💡 Usage

Testing the Server

Verify your installation works:

export GEMINI_API_KEY="your-api-key" cd gemini-mcp-server npm start

You should see: Gemini MCP Server running on stdio

Tool Examples

1. Basic Conversation

{ "tool": "gemini_chat", "arguments": { "message": "Explain the difference between async/await and Promises in JavaScript", "temperature": 0.7, "max_tokens": 2048 } }

Use Cases:

  • Quick questions and answers

  • Code generation

  • Content writing

  • Brainstorming ideas

2. Contextual Dialogue

{ "tool": "gemini_chat_with_history", "arguments": { "messages": [ { "role": "user", "parts": [{"text": "What is dependency injection?"}] }, { "role": "model", "parts": [{"text": "Dependency injection is a design pattern..."}] }, { "role": "user", "parts": [{"text": "Show me an example in TypeScript"}] } ], "temperature": 0.8 } }

Use Cases:

  • Technical tutorials

  • Iterative problem-solving

  • Learning sessions

  • Complex debugging

3. Code Analysis

{ "tool": "gemini_analyze_code", "arguments": { "code": "function processUser(data) {\n return data.name.toUpperCase();\n}", "language": "javascript", "analysis_type": "bugs" } }

Analysis Types:

  • bugs - Find errors and potential issues

  • optimize - Performance and best practices

  • explain - Detailed code explanation

  • review - Comprehensive assessment

📚 API Reference

Tool: gemini_chat

Description: Single-turn conversation with Gemini

Parameters:

Parameter

Type

Required

Default

Description

message

string

Yes

-

Your prompt or question

temperature

number

No

1.0

Creativity (0.0-2.0)

max_tokens

number

No

8192

Maximum response length

Example Response:

{ "content": [ { "type": "text", "text": "Here's a detailed explanation..." } ] }

Tool: gemini_chat_with_history

Description: Multi-turn conversation with context preservation

Parameters:

Parameter

Type

Required

Default

Description

messages

array

Yes

-

Conversation history

temperature

number

No

1.0

Creativity (0.0-2.0)

Message Format:

{ role: "user" | "model", parts: [{ text: string }] }

Tool: gemini_analyze_code

Description: AI-powered code analysis and review

Parameters:

Parameter

Type

Required

Default

Description

code

string

Yes

-

Code to analyze

language

string

No

-

Programming language

analysis_type

enum

No

review

bugs, optimize, explain, review

Supported Languages: JavaScript, TypeScript, Python, Go, Rust, Java, C++, Ruby, PHP, Swift, Kotlin, and more

🔧 Advanced Configuration

Environment Variables

# Required export GEMINI_API_KEY="your-api-key" # Optional (for custom deployments) export MCP_SERVER_PORT="3000" # If running as HTTP server export LOG_LEVEL="info" # debug, info, warn, error

Model Selection

To use different Gemini models, edit index.js:

const model = genAI.getGenerativeModel({ model: "gemini-2.5-pro", // or "gemini-2.0-flash-exp" generationConfig: { temperature: 1.0, maxOutputTokens: 8192, }, });

Available Models:

  • gemini-2.0-flash-exp - Fast, efficient (default)

  • gemini-2.5-pro - Most capable (when available)

  • gemini-pro - Balanced performance

🐛 Troubleshooting

Common Issues

Solution:

export GEMINI_API_KEY="your-key" # Or add to ~/.bashrc or ~/.zshrc for persistence echo 'export GEMINI_API_KEY="your-key"' >> ~/.bashrc

Checklist:

  1. Verify ~/.config/warp/mcp.json exists and is valid JSON

  2. Ensure paths in config use absolute paths or ${HOME}

  3. Completely quit and restart Warp (not just close window)

  4. Check Warp logs: Settings → Advanced → View Logs

Possible causes:

  • Invalid API key

  • API key not activated

  • Billing not enabled on Google Cloud

  • Rate limits exceeded

Solution: Verify your API key at Google AI Studio

Debug steps:

export GEMINI_API_KEY="your-key" node index.js 2>&1 | tee debug.log # Then check debug.log for errors

🔒 Security

Best Practices

  1. Never commit API keys - Use environment variables

  2. Rotate keys regularly - Generate new keys every 90 days

  3. Use key restrictions - Limit keys to specific APIs in Google Cloud Console

  4. Monitor usage - Check Google Cloud Console for unexpected activity

  5. Audit logs - Review MCP server logs periodically

Reporting Security Issues

Please report security vulnerabilities to info@cyberlinksec.com. Do not create public issues for security concerns.

See SECURITY.md for our full security policy.

🤝 Contributing

We welcome contributions! Here's how you can help:

Development Setup

# Clone the repo git clone https://github.com/bobvasic/gemini-mcp-server.git cd gemini-mcp-server # Install dependencies npm install # Run in development mode export GEMINI_API_KEY="your-key" npm start

Contribution Guidelines

  1. Fork the repository

  2. Create a feature branch (git checkout -b feature/amazing-feature)

  3. Commit your changes (git commit -m 'Add amazing feature')

  4. Push to the branch (git push origin feature/amazing-feature)

  5. Open a Pull Request

Code Standards

  • Follow existing code style

  • Add tests for new features

  • Update documentation

  • Ensure no hardcoded credentials

  • Use meaningful commit messages

📄 License

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

MIT License - Copyright (c) 2025 Gemini MCP Server Contributors

🙏 Acknowledgments

📊 Stats & Metrics

  • Response Time: < 2s average

  • Uptime: 99.9% (dependent on Google API)

  • Models Supported: 3+ Gemini variants

  • Languages: JavaScript/TypeScript

  • MCP Version: 1.0.4

🗺️ Roadmap

  • Add streaming response support

  • Implement token usage tracking

  • Add conversation history persistence

  • Support for image inputs

  • Multi-language documentation

  • Docker container support

  • Health check endpoints

  • Prometheus metrics export

Built with ❤️ for the developer community

If this project helped you, please ⭐ star the repository!

Documentation • Issues • Discussions

This server cannot be installed

-
security - not tested
-
license - not tested
-
quality - not tested

How are these scores calculated?

local-only server

The server can only run on the client's local machine because it depends on local resources.

Integrates Google Gemini AI into Warp terminal workflows through three focused tools: single-turn chat for quick questions, multi-turn conversations with context preservation, and AI-powered code analysis with bug detection and optimization suggestions.

  1. Customized for Warp Terminal
    1. Model Context Protocol Server for Google Gemini API
    2. Optimized for Modern Terminal Workflows
    3. 📋 Overview
    4. ✨ Features
    5. 🚀 Installation
    6. ⚙️ Configuration
    7. 💡 Usage
    8. 📚 API Reference
    9. 🔧 Advanced Configuration
    10. 🐛 Troubleshooting
    11. 🔒 Security
    12. 🤝 Contributing
    13. 📄 License
    14. 🙏 Acknowledgments
    15. 📊 Stats & Metrics
    16. 🗺️ Roadmap

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