ai-pair-programmer-mcp

# AI Pair Programmer MCP Server (Ruby) A Ruby implementation of an AI Pair Programmer MCP (Model Context Protocol) server that provides 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: ```bash export OPENROUTER_API_KEY="your_api_key_here" ``` ### Running the Server ```bash ruby ./server.rb ``` The server will: - Automatically install missing gems on first run - Start with STDIO transport for MCP clients - Log to `~/.claude-mcp-servers/ai-pair-programmer-mcp/` ### If the MCP fails to start when lunching Claude Code it's probably due to timeout. The bundler is installing dependecies. To fix that either set an envvar `MCP_TIMEOUT=10000` (10s) or start the MCP server in the terminal first. ## Configuration ### 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`: ```json { "mcpServers": { "AiPairProgrammer": { "command": "ruby", "args": ["/path/to/server.rb"] } } } ``` ## Usage Examples Once connected to an MCP client: ### 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? ``` ## Development ### Debugging Enable debug logging: ```bash DEBUG=1 ./server.rb ``` This will log detailed information about: - Tool calls and arguments - API requests and responses - Error details ### Log Files Logs are written to: ``` ~/.claude-mcp-servers/ai-pair-programmer-mcp/server.log ~/.claude-mcp-servers/ai-pair-programmer-mcp/ruby_llm.log ``` ## Architecture 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