Ai Pair Programmer MCP

🚀 AI Pair Programmer MCP Server (Ruby)

A Ruby implementation of an AI Pair Programmer MCP (Model Context Protocol) server, offering AI - powered tools for code review, brainstorming, performance analysis, and security review.

✨ Features

This server provides 5 AI - powered tools:

1. pair - General collaboration and problem - solving
2. review - Comprehensive code review with actionable feedback
3. brainstorm - Creative ideation and solution exploration
4. review_performance - Performance analysis and optimization suggestions
5. review_security - Security - focused code review and vulnerability detection

📦 Installation & Setup

Prerequisites

• Ruby 3.0+
• An OpenRouter API key

Automatic Installation

The server uses inline bundler for automatic gem installation. Required gems:

• fast - mcp - Ruby MCP server framework
• ruby_llm - Unified AI model interface

Configuration

1. Set your OpenRouter API key:

export OPENROUTER_API_KEY="your_api_key_here"

Running the Server

ruby ./server.rb

The server will:

• Automatically install missing gems on first run
• Start with STDIO transport for MCP clients
• Log to STDERR which should be saved by your MCP Host.

⚠️ Important Note

If the MCP fails to start when launching Claude Code, it's probably due to timeout. The bundler is installing dependencies. To fix that, either set an envvar MCP_TIMEOUT = 10000 (10s) or start the MCP server in the terminal first.

📚 Documentation

Models

The server supports these AI models via OpenRouter:

• Gemini (default) - google/gemini - 2.5 - pro - preview
• O3 - openai/o3
• Grok - x - ai/grok - 3 - beta
• DeepSeek - deepseek/deepseek - r1 - 0528
• Opus - anthropic/claude - opus - 4

MCP Client Configuration

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "AiPairProgrammer": {
      "command": "ruby",
      "args": ["/path/to/server.rb"]
    }
  }
}

💻 Usage Examples

Once connected to an MCP client:

Basic Usage

Code Review

Please review this Ruby code for best practices and potential issues:

def process_data(items)
  items.map { |item| item.upcase }
end

Performance Analysis

Can you analyze this code for performance bottlenecks?

def find_duplicates(array)
  duplicates = []
  array.each do |item|
    if array.count(item) > 1 && !duplicates.include?(item)
      duplicates << item
    end
  end
  duplicates
end

Security Review

Please check this authentication code for security vulnerabilities:

def authenticate(username, password)
  user = User.find_by(username: username)
  if user && user.password == password
    session[:user_id] = user.id
    true
  else
    false
  end
end

Brainstorming

I need ideas for improving user onboarding in my Ruby on Rails app. The current flow has a 60% drop - off rate.

General Collaboration

I'm struggling with this algorithm problem. Can you help me think through it step by step?

🔧 Technical Details

The server is built with:

• FastMcp - Ruby MCP server framework with STDIO transport
• RubyLLM - Unified interface to AI models via OpenRouter
• Inline Bundler - Automatic gem installation for easy deployment

📄 License

MIT License

Prior work:

AI Assistant MCP Server by Eduard https://github.com/eduardm/ai_pairs_with_ai

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test with ruby test_server.rb
5. Submit a pull request