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🧠 ACE MCP Server

\n \n

\n Self-Improving Context for Your AI Coding Assistant
\n Reduce tokens by 86.9%, improve accuracy by 10.6%\n

\n\n

\n \n $\"npm$ \n \n \n $\"License\"/$ \n \n \n $\"Research$ \n \n

\n\n

\n Quick Start •\n Features •\n How It Works •\n Documentation •\n Deployment\n

\n\n---\n\n## 🌟 Overview\n\n**ACE MCP Server** implements the Agentic Context Engineering framework as a Model Context Protocol (MCP) server for Cursor AI. Your AI assistant learns from its own execution feedback, building a self-improving knowledge base that gets better with every task.\n\nBased on [research](https://arxiv.org/pdf/2510.04618) from Stanford University & SambaNova Systems (October 2025).\n\n

\n \n \n \n \n \n \n \n

📉 86.9% Token Reduction
_{Incremental updates vs full rewrites}

📈 +10.6% Accuracy
_{Self-learning from feedback}

🔄 Continuous Improvement
_{Gets better with each use}

🎯 Context Isolation
_{Separate playbooks per domain}

\n\n---\n\n## 🎯 Why ACE?\n\nTraditional AI assistants forget everything between conversations. ACE remembers what works and what doesn't, creating a **playbook** of proven strategies that grows with your team's experience.\n\n### The Problem\n- 💸 High token costs from sending full context every time\n- 🔁 Same mistakes repeated across conversations\n- 📝 No learning from past successes/failures\n- 🤷 Generic responses that don't fit your codebase\n\n### The Solution\n- ✅ Incremental delta updates (send only changes)\n- ✅ Self-learning from execution feedback\n- ✅ Semantic deduplication (no redundant knowledge)\n- ✅ Context-aware strategies per domain\n\n---\n\n## ⚡ Quick Start\n\n### Prerequisites\n- Node.js 18+ \n- Cursor AI or MCP-compatible client\n- OpenAI API key OR local LM Studio server\n\n### Installation\n\n```bash\n# Clone repository\ngit clone https://github.com/Angry-Robot-Deals/ace-mcp.git\ncd ace-mcp\n\n# Install dependencies\nnpm install\n\n# Configure environment\ncp .env.example .env\n# Edit .env with your LLM provider settings\n\n# Build\nnpm run build\n\n# Start server\nnpm start\n```\n\n### Cursor AI Configuration\n\nAdd to `~/.cursor/mcp.json`:\n\n```json\n{\n \"mcpServers\": {\n \"ace-context-engine\": {\n \"command\": \"node\",\n \"args\": [\"/absolute/path/to/ace-mcp-server/dist/index.js\"],\n \"env\": {\n \"LLM_PROVIDER\": \"openai\",\n \"OPENAI_API_KEY\": \"sk-your-api-key-here\",\n \"ACE_CONTEXT_DIR\": \"./contexts\",\n \"ACE_LOG_LEVEL\": \"info\"\n }\n }\n }\n}\n```\n\n### Using Local LM Studio\n\n```json\n{\n \"mcpServers\": {\n \"ace-context-engine\": {\n \"command\": \"node\",\n \"args\": [\"/absolute/path/to/ace-mcp-server/dist/index.js\"],\n \"env\": {\n \"LLM_PROVIDER\": \"lmstudio\",\n \"LMSTUDIO_BASE_URL\": \"http://localhost:1234/v1\",\n \"LMSTUDIO_MODEL\": \"your-model-name\",\n \"ACE_CONTEXT_DIR\": \"./contexts\"\n }\n }\n }\n}\n```\n\n---\n\n## 🚀 Features\n\n### Core ACE Framework\n- **Generator**: Creates code using learned strategies\n- **Reflector**: Analyzes what worked and what didn't\n- **Curator**: Synthesizes insights into playbook updates\n\n### Smart Context Management\n- **Incremental Updates**: Only send deltas, not full context\n- **Semantic Deduplication**: Automatically merge similar strategies\n- **Multi-Context Support**: Separate playbooks for frontend, backend, DevOps, etc.\n- **Persistent Storage**: JSON-based storage with configurable backends\n\n### LLM Flexibility\n- **OpenAI Support**: Use GPT-4, GPT-3.5-turbo\n- **LM Studio Support**: Run local models offline\n- **Provider Abstraction**: Easy to add new LLM providers\n- **Configurable**: Switch providers via environment variables\n\n### Deployment Options\n- **Local Development**: Run on your machine\n- **Docker**: Full containerization support\n- **Ubuntu VM**: Production deployment ready\n- **Cloud**: Deploy to any Node.js-compatible platform\n\n---\n\n## 📊 How It Works\n\n```mermaid\ngraph LR\n A[Your Query] --> B[Generator]\n B --> C[Execute Code]\n C --> D[Reflector]\n D --> E[Extract Insights]\n E --> F[Curator]\n F --> G[Update Playbook]\n G --> H[Better Next Time]\n H --> B\n```\n\n### Example: Building an Authentication System\n\n1. **First Query**: \"Create login endpoint\"\n - Generator uses generic strategies\n - Creates basic endpoint\n - Reflector notices: \"Used bcrypt for passwords ✓\", \"Missing rate limiting ✗\"\n\n2. **Curator Updates Playbook**:\n - ADD: \"Always use bcrypt for password hashing\"\n - ADD: \"Include rate limiting on auth endpoints\"\n\n3. **Second Query**: \"Create registration endpoint\"\n - Generator automatically applies learned strategies\n - Includes bcrypt AND rate limiting from the start\n - Better code, fewer tokens, less iteration\n\n---\n\n## 🛠️ Available MCP Tools\n\n| Tool | Description | Use Case |\n|------|-------------|----------|\n| `ace_generate` | Generate code using playbook | Primary code generation |\n| `ace_reflect` | Analyze trajectory for insights | After code execution |\n| `ace_curate` | Convert insights to updates | Process reflections |\n| `ace_update_playbook` | Apply delta operations | Persist learned strategies |\n| `ace_get_playbook` | Retrieve current strategies | Review learned knowledge |\n| `ace_export_playbook` | Export as JSON | Backup or share playbooks |\n\n---\n\n## 📖 Documentation\n\n| Document | Description | Location |\n|----------|-------------|----------|\n| **Quick Start** | Installation and first steps | `docs/intro/START_HERE.md` |\n| **Full Specification** | Complete project details | `docs/intro/DESCRIPTION.md` |\n| **Installation Guide** | Detailed setup instructions | `docs/intro/INSTALLATION.md` |\n| **Memory Bank** | Project knowledge base | `memory-bank/` |\n\n---\n\n## 🐳 Docker Deployment\n\n### Local Development\n\n```bash\n# Start all services\ndocker-compose -f docker-compose.dev.yml up\n\n# Dashboard available at http://localhost:3000\n```\n\n### Production (Ubuntu VM)\n\n```bash\n# Configure environment\ncp .env.example .env\n# Edit .env with production settings\n\n# Start services\ndocker-compose up -d\n\n# View logs\ndocker-compose logs -f ace-server\n```\n\nSee `docs/intro/INSTALLATION.md` for detailed deployment guides.\n\n---\n\n## ⚙️ Configuration\n\n### Environment Variables\n\n```bash\n# LLM Provider Selection\nLLM_PROVIDER=openai # 'openai' or 'lmstudio'\n\n# OpenAI Configuration\nOPENAI_API_KEY=sk-...\nOPENAI_MODEL=gpt-4\nOPENAI_EMBEDDING_MODEL=text-embedding-3-small\n\n# LM Studio Configuration\nLMSTUDIO_BASE_URL=http://localhost:1234/v1\nLMSTUDIO_MODEL=your-model-name\n\n# ACE Settings\nACE_CONTEXT_DIR=./contexts # Storage directory\nACE_LOG_LEVEL=info # Logging level\nACE_DEDUP_THRESHOLD=0.85 # Similarity threshold (0-1)\nACE_MAX_PLAYBOOK_SIZE=1000 # Max bullets per context\n```\n\n---\n\n## 🏗️ Project Structure\n\n```\nace-mcp-server/\n├── src/\n│ ├── core/ # ACE components (Generator, Reflector, Curator)\n│ ├── mcp/ # MCP server and tools\n│ ├── storage/ # Bullet storage and deduplication\n│ ├── llm/ # LLM provider abstraction\n│ ├── utils/ # Utilities (config, logger, errors)\n│ └── index.ts # Entry point\n├── dashboard/ # Web dashboard (optional)\n├── docs/\n│ ├── intro/ # Documentation\n│ └── archive/ # Archived docs\n├── memory-bank/ # Project knowledge base\n├── docker-compose.yml # Production deployment\n├── docker-compose.dev.yml # Development deployment\n└── package.json\n```\n\n---\n\n## 🧪 Development\n\n```bash\n# Install dependencies\nnpm install\n\n# Run in development mode (with hot reload)\nnpm run dev\n\n# Build for production\nnpm run build\n\n# Run tests\nnpm test\n\n# Lint code\nnpm run lint\n```\n\n---\n\n## 📈 Performance Metrics\n\nBased on Stanford/SambaNova research:\n\n- **86.9% reduction** in context adaptation latency\n- **+10.6% improvement** in code generation accuracy\n- **30-50% reduction** in storage via semantic deduplication\n- **< 2s** for delta operations on 1K bullet playbooks\n\n---\n\n## 🤝 Contributing\n\nContributions are welcome! Please see our contributing guidelines.\n\n1. Fork the repository\n2. Create a feature branch\n3. Make your changes\n4. Write tests\n5. Submit a pull request\n\n---\n\n## 📄 License\n\nMIT License - see [LICENSE](LICENSE) file for details\n\n---\n\n## 🔗 Links\n\n- **Research Paper**: [Agentic Context Engineering](https://arxiv.org/pdf/2510.04618)\n- **MCP Specification**: [modelcontextprotocol.io](https://modelcontextprotocol.io)\n- **Cursor AI**: [cursor.sh](https://cursor.sh)\n- **GitHub**: [Angry-Robot-Deals/ace-mcp](https://github.com/Angry-Robot-Deals/ace-mcp)\n\n---\n\n## 💬 Support\n\n- 📧 **Email**: support@example.com\n- 💬 **Discussions**: [GitHub Discussions](https://github.com/Angry-Robot-Deals/ace-mcp/discussions)\n- 🐛 **Issues**: [GitHub Issues](https://github.com/Angry-Robot-Deals/ace-mcp/issues)\n- 📖 **Documentation**: See `docs/intro/` directory\n\n---\n\n## 🙏 Acknowledgments\n\nBased on research by:\n- Stanford University\n- SambaNova Systems\n\nPaper: \"Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models\" (October 2025)\n\n---\n\n

\n _{Built with ❤️ for developers who want their AI to learn and improve}\n

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MCP directory API