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Supports global, learned, and agent-specific memory types with markdown processing and duplicate detection.","displayName","MCP Memory Server","readme","# MCP Memory Server with Qdrant Vector Database\n\n🎉 **STATUS: PRODUCTION READY** ✅ | **All 9 Implementation Steps Complete**\n\nA Model Context Protocol (MCP) server that provides intelligent memory management capabilities using Qdrant vector database for semantic search and storage. Built specifically for Cursor IDE integration with comprehensive policy governance.\n\n## 🌟 Complete Feature Set\n\n🧠 **Multiple Memory Types**\n- **Global Memory**: Shared across all agents for common knowledge\n- **Learned Memory**: Lessons learned and mistakes to avoid \n- **Agent-Specific Memory**: Individual agent contexts and specialized knowledge\n\n🔍 **Advanced Search & Processing**\n- Vector-based similarity search using sentence transformers\n- Duplicate detection to prevent redundant content\n- Configurable similarity thresholds\n- Markdown processing with intelligent content cleaning\n- YAML front matter extraction and section organization\n\n🏛️ **Policy Governance System** *(NEW)*\n- **75 Enforceable Rules** across 4 categories (Principles, Forbidden Actions, Requirements, Style Guide)\n- **Semantic Policy Search** with vector embeddings for contextual rule discovery\n- **Compliance Tracking** with violation logging and agent accountability\n- **Version Management** with SHA-256 integrity verification\n- **Schema Validation** enforcing required sections and data consistency\n\n📋 **Prompt Management System** *(NEW)*\n- **Dynamic Template Engine** with variable substitution and conditional logic\n- **Agent-Specific Prompts** with custom configurations and role definitions\n- **Validation Framework** ensuring prompt consistency and formatting standards\n- **Context Integration** with memory and policy systems for enhanced prompts\n\n🔧 **Complete MCP Integration**\n- **9 MCP Tools**: Memory management, markdown processing, policy compliance, prompt generation\n- **4 MCP Resources**: Read-only access to memory, policies, prompts, and system status\n- Standard MCP protocol compliance for Cursor IDE\n- stdin/stdout communication with comprehensive error handling\n\n## Architecture\n\n```\n┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐\n│ │ │ │ │ │\n│ Cursor IDE │◄──►│ MCP Server │◄──►│ Qdrant Vector │\n│ │ │ (stdin/stdout) │ │ Database │\n│ │ │ │ │ │\n└─────────────────┘ └──────────────────┘ └─────────────────┘\n │\n ▼\n ┌──────────────────┐\n │ Sentence │\n │ Transformers │\n │ (Embeddings) │\n └──────────────────┘\n```\n\n## Installation\n\n### Prerequisites\n\n1. **Python 3.10+** with pip\n2. **Qdrant Database** (can run locally with Docker)\n3. **Cursor IDE** for MCP integration\n\n### Setup Qdrant Database\n\nUsing Docker (recommended):\n\n```bash\ndocker run -p 6333:6333 -v $(pwd)/qdrant_storage:/qdrant/storage qdrant/qdrant\n```\n\nOr install Qdrant locally following their [installation guide](https://qdrant.tech/documentation/quick-start/).\n\n### Install Dependencies\n\nUsing Poetry (recommended):\n\n```bash\n# Install dependencies using Poetry\npoetry install\n```\n\nOr using pip:\n\n```bash\n# Install Python dependencies\npip install -r requirements.txt\n```\n\n### Configuration\n\n1. Copy the example environment file:\n```bash\ncp .env.example .env\n```\n\n2. Edit `.env` with your settings:\n```env\n# Qdrant Configuration\nQDRANT_HOST=localhost\nQDRANT_PORT=6333\nQDRANT_API_KEY=\n\n# Embedding Model Configuration \nEMBEDDING_MODEL=all-MiniLM-L6-v2\nEMBEDDING_DIMENSION=384\n\n# Memory Configuration\nSIMILARITY_THRESHOLD=0.8\nMAX_RESULTS=10\n\n# Agent Configuration\nDEFAULT_AGENT_ID=default\n\n# Server Configuration\nLOG_LEVEL=INFO\n```\n\n## Usage\n\n### Starting the Server\n\n```bash\npython server.py\n```\n\nThe server will:\n1. Connect to Qdrant database\n2. Initialize vector collections\n3. Load the embedding model\n4. Start listening for MCP commands via stdin/stdout\n\n### Cursor IDE Integration\n\nAdd the server to your Cursor MCP configuration:\n\n```json\n{\n \"mcpServers\": {\n \"memory-server\": {\n \"command\": \"/media/hannesn/storage/Code/MCP/.venv/bin/python\",\n \"args\": [\"/media/hannesn/storage/Code/MCP/server.py\"],\n \"cwd\": \"/media/hannesn/storage/Code/MCP\",\n \"env\": {\n \"PYTHONPATH\": \"/media/hannesn/storage/Code/MCP\",\n \"QDRANT_HOST\": \"localhost\",\n \"QDRANT_PORT\": \"6333\",\n \"EMBEDDING_MODEL\": \"all-MiniLM-L6-v2\",\n \"SIMILARITY_THRESHOLD\": \"0.8\",\n \"MAX_RESULTS\": \"10\",\n \"LOG_LEVEL\": \"INFO\"\n }\n }\n }\n}\n```\n\nAlternatively, you can run the server using Poetry:\n\n```bash\npoetry run python server.py\n```\n\n## MCP Tools\n\n### 1. `set_agent_context`\n\nInitialize agent context from a markdown file.\n\n**Parameters:**\n- `agent_id` (string): Unique identifier for the agent\n- `context_file_path` (string): Path to markdown file with agent context\n- `description` (string, optional): Description of the context\n\n**Example:**\n```json\n{\n \"tool\": \"set_agent_context\",\n \"arguments\": {\n \"agent_id\": \"frontend_dev\",\n \"context_file_path\": \"./contexts/frontend_agent.md\",\n \"description\": \"Frontend development agent context\"\n }\n}\n```\n\n### 2. `add_to_global_memory`\n\nAdd content to global memory shared across all agents.\n\n**Parameters:**\n- `file_path` (string): Path to markdown file\n- `description` (string, optional): Content description\n\n**Example:**\n```json\n{\n \"tool\": \"add_to_global_memory\", \n \"arguments\": {\n \"file_path\": \"./docs/coding_standards.md\",\n \"description\": \"Company coding standards\"\n }\n}\n```\n\n### 3. `add_to_learned_memory`\n\nStore lessons learned to avoid repeated mistakes.\n\n**Parameters:**\n- `file_path` (string): Path to markdown file with lessons\n- `lesson_type` (string): Type of lesson (e.g., \"deployment\", \"security\")\n- `description` (string, optional): Lesson description\n\n**Example:**\n```json\n{\n \"tool\": \"add_to_learned_memory\",\n \"arguments\": {\n \"file_path\": \"./lessons/deployment_issues.md\", \n \"lesson_type\": \"deployment\",\n \"description\": \"Critical deployment lessons\"\n }\n}\n```\n\n### 4. `add_to_agent_memory`\n\nAdd content to agent-specific memory.\n\n**Parameters:**\n- `agent_id` (string): Target agent identifier\n- `file_path` (string): Path to markdown file\n- `description` (string, optional): Content description\n\n**Example:**\n```json\n{\n \"tool\": \"add_to_agent_memory\",\n \"arguments\": {\n \"agent_id\": \"backend_dev\",\n \"file_path\": \"./docs/api_patterns.md\",\n \"description\": \"Backend API design patterns\"\n }\n}\n```\n\n### 5. `query_memory`\n\nSearch memory collections for relevant content.\n\n**Parameters:**\n- `query` (string): Search query\n- `memory_type` (string): \"global\", \"learned\", \"agent\", or \"all\" \n- `agent_id` (string, optional): Agent ID for agent-specific queries\n- `max_results` (integer, optional): Maximum results (default: 10)\n\n**Example:**\n```json\n{\n \"tool\": \"query_memory\",\n \"arguments\": {\n \"query\": \"authentication best practices\",\n \"memory_type\": \"all\",\n \"max_results\": 5\n }\n}\n```\n\n### 6. `compare_against_learned_memory`\n\nCheck proposed actions against past lessons learned.\n\n**Parameters:**\n- `action_description` (string): Description of proposed action\n- `agent_id` (string, optional): Agent making the request\n\n**Example:**\n```json\n{\n \"tool\": \"compare_against_learned_memory\",\n \"arguments\": {\n \"action_description\": \"Deploy database migration on Friday afternoon\",\n \"agent_id\": \"devops_agent\"\n }\n}\n```\n\n## Memory Types Explained\n\n### Global Memory\n- **Purpose**: Store knowledge shared across all agents\n- **Content**: Coding standards, documentation, best practices\n- **Access**: All agents can query this memory\n- **Use Case**: Company-wide policies, architectural decisions\n\n### Learned Memory \n- **Purpose**: Store lessons learned from past mistakes\n- **Content**: Incident reports, post-mortems, anti-patterns\n- **Access**: Most agents (exclude \"human-like\" testers)\n- **Use Case**: Avoid repeating past mistakes, improve decisions\n\n### Agent-Specific Memory\n- **Purpose**: Store knowledge specific to individual agents\n- **Content**: Role definitions, specialized knowledge, context\n- **Access**: Only the specific agent\n- **Use Case**: Agent initialization, specialized expertise\n\n## Testing\n\n### Run Basic Functionality Tests\n\n```bash\npython tests/test_basic_functionality.py\n```\n\nThis will test:\n- Qdrant connection and collection setup\n- Memory operations (add, query, duplicate detection)\n- Markdown processing and content cleaning\n- Vector embedding and similarity search\n\n### Manual Testing with Sample Data\n\n1. Start the server:\n```bash\npython server.py\n```\n\n2. Use the provided sample markdown files in `sample_data/`:\n - `frontend_agent_context.md`: Frontend agent context\n - `backend_agent_context.md`: Backend agent context \n - `deployment_lessons.md`: Learned lessons\n - `global_standards.md`: Global development standards\n\n## Troubleshooting\n\n### Common Issues\n\n**Qdrant Connection Failed**\n```\n❌ Failed to initialize Qdrant: ConnectionError\n```\n- Ensure Qdrant is running on configured host/port\n- Check firewall settings\n- Verify API key if using Qdrant Cloud\n\n**Embedding Model Download Issues**\n```\n❌ Failed to load embedding model\n```\n- Ensure internet connection for first download\n- Check available disk space (models can be large)\n- Try alternative model in configuration\n\n**Memory Full / Performance Issues**\n- Reduce `EMBEDDING_DIMENSION` for smaller models\n- Increase `SIMILARITY_THRESHOLD` to reduce results\n- Consider pruning old content from collections\n\n### Debugging\n\nEnable debug logging:\n```bash\nexport LOG_LEVEL=DEBUG\npython server.py\n```\n\nCheck Qdrant collections:\n```bash\ncurl http://localhost:6333/collections\n```\n\n## Configuration Reference\n\n### Environment Variables\n\n| Variable | Default | Description |\n|----------|---------|-------------|\n| `QDRANT_HOST` | localhost | Qdrant server host |\n| `QDRANT_PORT` | 6333 | Qdrant server port |\n| `QDRANT_API_KEY` | | API key for Qdrant Cloud |\n| `EMBEDDING_MODEL` | all-MiniLM-L6-v2 | Sentence transformer model |\n| `EMBEDDING_DIMENSION` | 384 | Vector dimension size |\n| `SIMILARITY_THRESHOLD` | 0.8 | Duplicate detection threshold |\n| `MAX_RESULTS` | 10 | Default max query results |\n| `DEFAULT_AGENT_ID` | default | Default agent identifier |\n| `LOG_LEVEL` | INFO | Logging verbosity |\n\n### Collection Names\n\n- **Global Memory**: `global_memory`\n- **Learned Memory**: `learned_memory` \n- **Agent Memory**: `agent_specific_memory_{agent_id}`\n\n## Development\n\n### Project Structure\n\n```\nmcp-memory-server/\n├── server.py # Main MCP server\n├── src/\n│ ├── __init__.py\n│ ├── config.py # Configuration management\n│ ├── memory_manager.py # Qdrant operations\n│ └── markdown_processor.py # Markdown handling\n├── tests/\n│ └── test_basic_functionality.py\n├── sample_data/ # Example markdown files\n├── docs/ # Additional documentation\n├── requirements.txt # Python dependencies\n├── pyproject.toml # Poetry configuration\n└── README.md # This file\n```\n\n### Contributing\n\n1. Fork the repository\n2. Create a feature branch\n3. Make your changes\n4. Run tests: `python -m pytest tests/`\n5. Submit a pull request\n\n### Adding New Tools\n\n1. Add tool function to `MCPMemoryServer._register_tools()`\n2. Update `_list_tools()` method with tool schema\n3. Add tests for the new functionality\n4. Update this README\n\n## License\n\nMIT License - see LICENSE file for details.\n\n## Support\n\nFor issues and questions:\n1. Check the troubleshooting section above\n2. Review Qdrant documentation for database issues\n3. Check MCP protocol documentation for integration issues\n4. 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