MCP Remote macOS Control Server

# AI macOS Control Web App - Development Plan ## 🎯 Project Overview **Goal**: Build a web-based AI chatbot that can control macOS through the existing MCP server **Target**: Proof of Concept → Production Ready App **Timeline**: 2-3 weeks for MVP, 4-6 weeks for production version ## 🏗️ Architecture Overview ``` ┌─────────────────────┐ ┌─────────────────────┐ ┌─────────────────────┐ │ Frontend │────▶│ Backend │────▶│ MCP Server │ │ (React/Next.js) │ │ (Node.js/Express) │ │ (Python) │ │ │ │ │ │ │ │ • Chat Interface │ │ • WebSocket Server │ │ • macOS Control │ │ • Real-time Updates │ │ • LLM Integration │ │ • VNC Client │ │ • Image Display │ │ • MCP Client │ │ • Action Handlers │ │ • Loading States │ │ • Session Management│ │ │ └─────────────────────┘ └─────────────────────┘ └─────────────────────┘ │ │ │ │ │ │ WebSocket/HTTP LLM APIs Local macOS ``` ## 📚 Technology Stack ### Frontend - **Framework**: Next.js 14 (React + TypeScript) - **Styling**: Tailwind CSS + Headless UI - **Real-time**: Socket.IO Client - **State Management**: Zustand (lightweight) - **HTTP Client**: Axios - **UI Components**: Custom chat components ### Backend - **Runtime**: Node.js 18+ - **Framework**: Express.js - **Real-time**: Socket.IO - **MCP Client**: Custom implementation - **LLM Integration**: OpenAI SDK / Anthropic SDK - **Process Management**: PM2 (production) ### DevOps & Tools - **Package Manager**: npm/yarn - **Development**: Nodemon, Concurrently - **Testing**: Jest, Cypress - **Linting**: ESLint, Prettier - **Version Control**: Git ## 📁 Project Structure ``` macos-ai-chat/ ├── README.md ├── package.json ├── next.config.js ├── tailwind.config.js ├── tsconfig.json │ ├── frontend/ # Next.js Frontend │ ├── src/ │ │ ├── components/ │ │ │ ├── Chat/ │ │ │ │ ├── ChatInterface.tsx │ │ │ │ ├── MessageBubble.tsx │ │ │ │ ├── InputArea.tsx │ │ │ │ └── TypingIndicator.tsx │ │ │ ├── UI/ │ │ │ │ ├── Button.tsx │ │ │ │ ├── Loading.tsx │ │ │ │ └── Modal.tsx │ │ │ └── Layout/ │ │ │ ├── Header.tsx │ │ │ └── Sidebar.tsx │ │ ├── hooks/ │ │ │ ├── useSocket.ts │ │ │ ├── useChat.ts │ │ │ └── useLocalStorage.ts │ │ ├── stores/ │ │ │ ├── chatStore.ts │ │ │ └── settingsStore.ts │ │ ├── types/ │ │ │ ├── chat.ts │ │ │ └── api.ts │ │ ├── utils/ │ │ │ ├── formatters.ts │ │ │ └── constants.ts │ │ └── pages/ │ │ ├── index.tsx │ │ ├── settings.tsx │ │ └── api/ │ └── public/ │ ├── favicon.ico │ └── images/ │ ├── backend/ # Express.js Backend │ ├── src/ │ │ ├── server.ts │ │ ├── config/ │ │ │ ├── environment.ts │ │ │ └── socket.ts │ │ ├── services/ │ │ │ ├── mcpClient.ts │ │ │ ├── llmService.ts │ │ │ ├── chatService.ts │ │ │ └── sessionService.ts │ │ ├── controllers/ │ │ │ ├── chatController.ts │ │ │ └── healthController.ts │ │ ├── middleware/ │ │ │ ├── errorHandler.ts │ │ │ ├── validation.ts │ │ │ └── rateLimit.ts │ │ ├── routes/ │ │ │ ├── chat.ts │ │ │ └── health.ts │ │ ├── types/ │ │ │ ├── mcp.ts │ │ │ ├── llm.ts │ │ │ └── chat.ts │ │ └── utils/ │ │ ├── logger.ts │ │ └── helpers.ts │ └── package.json │ ├── docker/ │ ├── Dockerfile.frontend │ ├── Dockerfile.backend │ └── docker-compose.yml │ ├── docs/ │ ├── API.md │ ├── DEPLOYMENT.md │ └── TESTING.md │ └── scripts/ ├── dev.sh ├── build.sh └── deploy.sh ``` ## 🚀 Development Phases ### Phase 1: Foundation (Week 1) **Duration**: 3-4 days **Goal**: Basic working prototype #### 1.1 Project Setup (Day 1) - [ ] Initialize Next.js project with TypeScript - [ ] Set up Express.js backend - [ ] Configure Tailwind CSS - [ ] Set up basic project structure - [ ] Install and configure dependencies #### 1.2 Basic Chat Interface (Day 2) - [ ] Create basic chat layout - [ ] Implement message bubbles (user/assistant) - [ ] Add input area with send functionality - [ ] Set up WebSocket connection (frontend) - [ ] Basic styling and responsive design #### 1.3 Backend Foundation (Day 3) - [ ] Set up Express server with Socket.IO - [ ] Create basic WebSocket handlers - [ ] Implement health check endpoint - [ ] Set up environment configuration - [ ] Add basic logging #### 1.4 MCP Integration (Day 4) - [ ] Create MCP client service - [ ] Implement basic tool calling - [ ] Test screenshot functionality - [ ] Add error handling - [ ] Connect frontend to backend ### Phase 2: Core Features (Week 2) **Duration**: 5-6 days **Goal**: Full AI integration with macOS control #### 2.1 LLM Integration (Days 1-2) - [ ] Set up OpenAI/Anthropic API client - [ ] Implement function calling workflow - [ ] Create tool schema conversion (MCP → LLM format) - [ ] Add conversation context management - [ ] Implement streaming responses #### 2.2 Advanced Chat Features (Days 3-4) - [ ] Add typing indicators - [ ] Implement message history persistence - [ ] Add image display for screenshots - [ ] Create loading states for tool execution - [ ] Add message timestamps and status #### 2.3 macOS Control Integration (Days 5-6) - [ ] Integrate all MCP tools: - [ ] Screenshot capture - [ ] Mouse click/move/scroll - [ ] Keyboard input - [ ] Application launching - [ ] Drag and drop - [ ] Add tool execution feedback - [ ] Implement coordinate scaling - [ ] Add safety confirmations for destructive actions ### Phase 3: Polish & Production (Week 3) **Duration**: 5-7 days **Goal**: Production-ready application #### 3.1 User Experience (Days 1-2) - [ ] Improve UI/UX design - [ ] Add dark/light theme - [ ] Implement settings panel - [ ] Add keyboard shortcuts - [ ] Mobile-responsive design #### 3.2 Error Handling & Validation (Days 3-4) - [ ] Comprehensive error handling - [ ] Input validation and sanitization - [ ] Rate limiting and spam protection - [ ] Connection retry logic - [ ] Graceful degradation #### 3.3 Testing & Deployment (Days 5-7) - [ ] Unit tests for critical functions - [ ] Integration tests for MCP communication - [ ] E2E tests for chat workflow - [ ] Performance optimization - [ ] Docker containerization - [ ] Deployment scripts ## 🔧 Key Implementation Details ### WebSocket Event Schema ```typescript // Client → Server interface ClientEvents { 'chat_message': { message: string; sessionId: string; }; 'join_session': { sessionId: string; }; } // Server → Client interface ServerEvents { 'chat_response': { message: string; type: 'text' | 'image' | 'error'; timestamp: number; }; 'typing_start': {}; 'typing_stop': {}; 'tool_execution_start': { toolName: string; }; 'tool_execution_complete': { toolName: string; success: boolean; }; } ``` ### MCP Tool Integration ```typescript interface MCPTool { name: string; description: string; inputSchema: object; } interface ToolCall { name: string; arguments: Record<string, any>; } interface ToolResult { success: boolean; data?: any; error?: string; } ``` ### LLM Integration Flow ```typescript // 1. User sends message // 2. Check if message requires tools // 3. Call LLM with available tools // 4. Execute tool calls via MCP // 5. Send results back to LLM // 6. Return final response to user ``` ## 🧪 Testing Strategy ### Unit Tests - MCP client functions - LLM service integration - Chat message processing - Tool result parsing ### Integration Tests - WebSocket communication - MCP server connectivity - End-to-end tool execution - Error handling flows ### E2E Tests - Complete chat workflow - Screenshot capture and display - Mouse/keyboard control - Application launching ## 📊 Success Metrics ### Phase 1 Success Criteria - [ ] Chat interface loads and displays messages - [ ] WebSocket connection established - [ ] Basic screenshot tool works - [ ] No critical errors in console ### Phase 2 Success Criteria - [ ] All MCP tools integrated and working - [ ] LLM responds with appropriate tool calls - [ ] Real-time updates work smoothly - [ ] Error states handled gracefully ### Phase 3 Success Criteria - [ ] App works reliably for 30+ minutes - [ ] Responsive design on different screen sizes - [ ] Performance under normal usage loads - [ ] Ready for user testing ## 🚀 Development Commands ```bash # Development npm run dev # Start both frontend and backend npm run dev:frontend # Start only frontend npm run dev:backend # Start only backend # Building npm run build # Build both frontend and backend npm run build:frontend npm run build:backend # Testing npm run test # Run all tests npm run test:unit # Unit tests only npm run test:e2e # E2E tests only # Deployment npm run deploy:staging npm run deploy:production ``` ## 📋 Environment Variables ```bash # Backend Environment NODE_ENV=development PORT=3001 OPENAI_API_KEY=your_openai_key ANTHROPIC_API_KEY=your_anthropic_key # MCP Server Configuration MACOS_HOST=localhost MACOS_PASSWORD=your_vnc_password MACOS_USERNAME=your_username MACOS_PORT=5900 # Optional: LiveKit for WebRTC LIVEKIT_URL=your_livekit_url LIVEKIT_API_KEY=your_api_key LIVEKIT_API_SECRET=your_api_secret ``` ## 🎯 Next Steps 1. **Confirm tech stack choices** 2. **Set up development environment** 3. **Begin Phase 1 implementation** 4. **Regular progress reviews** 5. **User testing and feedback** This plan provides a structured approach to building your AI macOS control web app. Each phase builds upon the previous one, ensuring steady progress toward a production-ready application. Would you like me to start implementing any specific part of this plan?