ACE MCP Server

# ACE MCP Server > **Agentic Context Engineering (ACE)** - Self-improving AI context framework with Model Context Protocol (MCP) integration for Cursor AI. ## 🎯 Overview ACE MCP Server is an intelligent development assistant that learns from your coding patterns and automatically enhances your development workflow. It integrates seamlessly with Cursor AI through the Model Context Protocol (MCP), providing contextual code generation, intelligent analysis, and self-improving recommendations. ### ✨ Key Features - **🤖 Smart Code Generation** - Context-aware code generation with automatic prompt enhancement - **🧠 Intelligent Code Analysis** - Deep code analysis with actionable improvement suggestions - **📚 Self-Improving Playbook** - Accumulates knowledge and patterns from your development work - **🔧 Multiple LLM Support** - Works with OpenAI, Anthropic Claude, DeepSeek, Google Gemini, Mistral, and LM Studio - **🐳 Docker Ready** - Complete containerized solution for local and production deployment - **🔒 Secure by Default** - Bearer token authentication and comprehensive security measures ### 🚀 What Makes ACE Special ACE doesn't just generate code - it **learns** from your development patterns and **improves** over time: 1. **Generates** contextual development trajectories 2. **Reflects** on code to extract insights and patterns 3. **Curates** knowledge into a self-improving playbook 4. **Enhances** future interactions with accumulated wisdom ## 📚 Documentation ### 🚀 Getting Started - **[Installation Guide](./docs/intro/INSTALLATION.md)** - Complete setup instructions - **[Project Overview](./docs/intro/START_HERE.md)** - Detailed project introduction - **[Quick Start](./docs/intro/DESCRIPTION.md)** - Fast track to running ACE ### ⚙️ Setup & Configuration - **[Cursor AI Setup](./docs/setup/CURSOR_AI_SETUP.md)** - Basic MCP integration - **[Enhanced Auto Setup](./docs/setup/CURSOR_AI_AUTO_SETUP.md)** - Smart auto-enhancement features - **[LLM Providers](./docs/LLM_PROVIDERS.md)** - Configure different AI providers ### 🚀 Deployment - **[Production Deployment](./docs/deployment/DEPLOYMENT_README.md)** - Deploy to production servers - **[Full Deployment Guide](./docs/intro/DEPLOYMENT.md)** - Complete Docker deployment guide ### 📖 Project Documentation - **[Project Status](./docs/intro/PROJECT_STATUS.md)** - Current development status - **[Architecture](./docs/intro/INITIALIZATION_REPORT.md)** - Technical architecture details - **[GitHub Setup](./docs/intro/GITHUB_INITIALIZATION.md)** - Repository initialization ## ⚡ Quick Start ### 1. Clone and Setup ```bash git clone https://github.com/Angry-Robot-Deals/ace-mcp.git cd ace-mcp cp .env.example .env # Edit .env with your configuration ``` ### 2. Docker Development ```bash # Start development environment docker-compose -f docker-compose.dev.yml up -d # View logs docker-compose -f docker-compose.dev.yml logs -f # Stop environment docker-compose -f docker-compose.dev.yml down ``` ### 3. Configure Cursor AI See detailed setup instructions: - **[Basic Cursor AI Setup](./docs/setup/CURSOR_AI_SETUP.md)** - Initialize your MCP server with basic ACE tools - **[Enhanced Auto Setup](./docs/setup/CURSOR_AI_AUTO_SETUP.md)** - Automatically enhance prompts and invoke appropriate ACE methods ### 4. Use ACE Commands ```bash # Smart code generation @ace_smart_generate create a REST API endpoint # Intelligent code analysis @ace_smart_reflect [your code here] # Context-aware assistance @ace_context_aware optimize database queries domain:database # Automatic prompt enhancement @ace_enhance_prompt create secure authentication focus_area:security # View current playbook @ace_playbook ``` ### 5. View Playbook The ACE playbook stores accumulated knowledge and patterns from your development work. View it using: **Option 1: Via API endpoint (JSON)** ```bash curl -H 'Authorization: Bearer YOUR_TOKEN' \ http://localhost:34301/api/playbook | python3 -m json.tool ``` **Option 2: Using provided script** ```bash ./view-playbook.sh ``` **Option 3: Via MCP tool in Cursor AI** ``` @ace_playbook ``` **Option 4: Via dashboard** ``` http://localhost:34300 ``` The playbook contains: - **Patterns** - Code patterns and conventions learned from your work - **Best Practices** - Development best practices accumulated over time - **Insights** - Key insights extracted from code reflections ## 🛠️ Development ### Prerequisites - Node.js 18+ - Docker & Docker Compose - TypeScript ### Local Development ```bash # Install dependencies npm install # Run tests npm test # Build project npm run build # Start development server npm run dev ``` ### Docker Management ```bash # Development environment docker-compose -f docker-compose.dev.yml up -d # Production environment docker-compose up -d # View service logs docker-compose logs ace-server docker-compose logs ace-dashboard # Rebuild services docker-compose build --no-cache ``` ## 🔧 Configuration ### LLM Providers & Models ACE supports 6 LLM providers with various models: #### Supported Providers 1. **DeepSeek** (Recommended) ⭐ - Provider: `deepseek` - Default Model: `deepseek-chat` (V3.2-Exp) - Embedding Model: `deepseek-embedding` - Best for: ACE framework performance, cost-effective - Pricing: $0.28/1M input, $0.42/1M output tokens - Context: 128K tokens, Max output: 32K (reasoner mode) - Environment Variables: ```bash LLM_PROVIDER=deepseek DEEPSEEK_API_KEY=sk-your-deepseek-api-key DEEPSEEK_MODEL=deepseek-chat DEEPSEEK_EMBEDDING_MODEL=deepseek-embedding ``` 2. **OpenAI** - Provider: `openai` - Models: `gpt-4o`, `gpt-4`, `gpt-3.5-turbo` - Embedding Models: `text-embedding-3-small`, `text-embedding-3-large` - Environment Variables: ```bash LLM_PROVIDER=openai OPENAI_API_KEY=sk-your-openai-api-key OPENAI_MODEL=gpt-4o OPENAI_EMBEDDING_MODEL=text-embedding-3-small ``` 3. **Anthropic Claude** - Provider: `anthropic` - Models: `claude-3-5-sonnet-20241022`, `claude-3-opus`, `claude-3-sonnet`, `claude-3-haiku` - Environment Variables: ```bash LLM_PROVIDER=anthropic ANTHROPIC_API_KEY=sk-ant-your-api-key ANTHROPIC_MODEL=claude-3-5-sonnet-20241022 ``` 4. **Google Gemini** - Provider: `gemini` - Models: `gemini-1.5-pro`, `gemini-1.5-flash`, `gemini-pro` - Environment Variables: ```bash LLM_PROVIDER=gemini GOOGLE_API_KEY=your-google-api-key GOOGLE_MODEL=gemini-1.5-pro ``` 5. **Mistral** - Provider: `mistral` - Models: `mistral-large-latest`, `mistral-medium-latest`, `mistral-small-latest` - Environment Variables: ```bash LLM_PROVIDER=mistral MISTRAL_API_KEY=your-mistral-api-key MISTRAL_MODEL=mistral-large-latest ``` 6. **LM Studio** (Local/Self-hosted) - Provider: `lmstudio` - Models: Any local model compatible with OpenAI API format - Environment Variables: ```bash LLM_PROVIDER=lmstudio LMSTUDIO_BASE_URL=http://localhost:1234/v1 LMSTUDIO_MODEL=local-model ``` ### Environment Variables Copy `.env.example` to `.env` and configure: ```bash # LLM Provider Selection (required) # Options: 'deepseek', 'openai', 'anthropic', 'gemini', 'mistral', 'lmstudio' LLM_PROVIDER=deepseek # Provider-specific API keys and models (see above for details) DEEPSEEK_API_KEY=sk-your-deepseek-api-key DEEPSEEK_MODEL=deepseek-chat # Server Configuration ACE_SERVER_PORT=34301 DASHBOARD_PORT=34300 API_BEARER_TOKEN=your-secure-token # Docker Configuration COMPOSE_PROJECT_NAME=ace-mcp DOCKER_BUILDKIT=1 ``` For complete configuration options, see `.env.example` file. ## 🤝 Contributing 1. **Read the Documentation** - Start with [Project Overview](./docs/intro/START_HERE.md) 2. **Follow Best Practices** - Review [Development Guide](./docs/intro/DESCRIPTION.md) 3. **Submit PRs** - [Follow our contribution guidelines](./docs/CONTRIBUTING.md) ## 📄 License This project is licensed under the MIT License - see the LICENSE file for details. ## 🔗 Links - **[GitHub Repository](https://github.com/Angry-Robot-Deals/ace-mcp)** - **[Issues & Bug Reports](https://github.com/Angry-Robot-Deals/ace-mcp/issues)** - **[Documentation](./docs/)** --- **ACE MCP Server - Making AI development smarter, one interaction at a time.** 🚀