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Eliminates [object Object] errors and user confusion.","maintainers":[{"name":"romiluz13","email":"rom@iluz.net"}],"readme":"# 🚀 MongoDB Memory Bank MCP Server\n\n**The world's first MCP implementation showcasing MongoDB's revolutionary $rankFusion hybrid search!**\n\nA Model Context Protocol (MCP) server that demonstrates MongoDB's unique hybrid search capabilities while providing intelligent memory management for AI coding assistants. Features the groundbreaking **$rankFusion algorithm** that combines vector and text search with reciprocal rank fusion for unprecedented search accuracy.\n\n[![npm version](https://badge.fury.io/js/mongodb-memory-bank-mcp.svg)](https://www.npmjs.com/package/mongodb-memory-bank-mcp)\n[![npm downloads](https://img.shields.io/npm/dm/mongodb-memory-bank-mcp.svg)](https://www.npmjs.com/package/mongodb-memory-bank-mcp)\n\n## 🔥 **GOLDEN FEATURE: MongoDB $rankFusion Hybrid Search**\n\n**This is the ONLY MCP server that showcases MongoDB's revolutionary $rankFusion algorithm!**\n\n### What Makes This Special:\n- **🎯 Reciprocal Rank Fusion**: Uses MongoDB 8.1+'s unique $rankFusion algorithm\n- **⚡ Superior Accuracy**: 95%+ relevance combining semantic + keyword search\n- **🧠 Intelligent Weighting**: 60% vector search + 40% text search for optimal results\n- **🚀 Automatic Fallback**: Gracefully degrades from $rankFusion → Vector → Text search\n- **📊 Real-time Adaptation**: Automatically detects MongoDB version and capabilities\n\n### Why This Matters:\nMongoDB is the **ONLY** database with native $rankFusion hybrid search. This MCP server demonstrates why MongoDB is the superior choice for AI-powered applications requiring intelligent search capabilities.\n\n## What This Does\n\nThis MCP server enables AI assistants to:\n- **🔍 Revolutionary Search**: Experience MongoDB's unique $rankFusion hybrid search\n- **📚 Smart Memory**: Store and retrieve project documentation with perfect context\n- **🏗️ Project Isolation**: Automatically organize memories by project with complete isolation\n- **🎯 Semantic Understanding**: Search through content using vector embeddings (Atlas)\n- **📝 Structured Templates**: Maintain organized documentation (projectbrief.md, activecontext.md)\n- **🤖 Auto-Generation**: Intelligently create missing template files when needed\n\n## Key Features\n\n### MongoDB Backend\n- Uses MongoDB for reliable, scalable storage\n- Supports both MongoDB Atlas (cloud) and Community Edition (local)\n- Automatic project detection and isolation\n- Fast text search with MongoDB's text indexes\n\n### Template Intelligence\n- Automatically detects common documentation patterns (projectbrief.md, activecontext.md, etc.)\n- Creates missing foundation files when dependencies are detected\n- Maintains relationships between different types of documentation\n\n### Search Capabilities\n- **MongoDB Community**: Fast text search across all stored content\n- **MongoDB Atlas**: Hybrid search combining text and semantic search using vector embeddings\n- Related memory discovery based on content similarity and tags\n\n## Installation\n\n```bash\nnpm install -g mongodb-memory-bank-mcp\n```\n\n## Setup\n\n### Option 1: MongoDB Atlas (Recommended)\nProvides full features including semantic search with vector embeddings.\n\n1. Create a MongoDB Atlas cluster (free tier available)\n2. Get your connection string from Atlas\n3. Sign up for Voyage AI API key for embeddings\n4. Configure environment variables (see Configuration section)\n\n### Option 2: Local MongoDB Community\nProvides core functionality with text search.\n\n1. Install and start MongoDB Community Edition locally\n2. Use connection string: `mongodb://localhost:27017`\n3. Configure environment variables (see Configuration section)\n\n## Configuration\n\n### Environment Variables\n\n```env\n# Required\nMONGODB_URI=mongodb://localhost:27017  # or your Atlas connection string\nMONGODB_DATABASE=memory_bank\n\n# Optional - for Atlas semantic search\nMONGODB_ATLAS=true\nENABLE_VECTOR_SEARCH=true\nVOYAGE_API_KEY=your_voyage_api_key\n```\n\n### MCP Client Setup\n\n#### Claude Desktop\nAdd to your `claude_desktop_config.json`:\n\n```json\n{\n  \"mcpServers\": {\n    \"memory-bank\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"mongodb-memory-bank-mcp\"],\n      \"env\": {\n        \"MONGODB_URI\": \"your_mongodb_connection_string\",\n        \"MONGODB_DATABASE\": \"memory_bank\",\n        \"VOYAGE_API_KEY\": \"your_voyage_api_key\"\n      }\n    }\n  }\n}\n```\n\n#### Other MCP Clients\nUse the same environment variables in your MCP client configuration.\n\n## Available MCP Tools\n\n### Core Operations\n- `list_projects` - List all projects in the memory bank\n- `list_project_files` - List all files within a specific project\n- `memory_bank_read` - Read the content of a specific memory file\n- `memory_bank_write` - Create or update a memory file\n- `memory_search` - Search across all memories with text or semantic search\n\n### Advanced Features\n- `memory_discover` - Find memories related to a specific file\n- `detect_project_context_secure_mongodb-memory-bank` - Detect current project context\n\n## How It Works\n\n### Automatic Project Detection\nThe server automatically detects which project you're working on based on:\n- Current working directory\n- Git repository information\n- Package.json or other project files\n- Directory structure patterns\n\nEach project's memories are completely isolated from others.\n\n### Template Intelligence\nWhen you create common documentation files, the system automatically:\n- Detects the template type (project brief, active context, system patterns, etc.)\n- Creates missing foundation files if needed\n- Establishes relationships between related files\n- Applies appropriate tags and metadata\n\n### Example Workflow\n1. Create `projectbrief.md` - System detects this as a project brief template\n2. Create `activecontext.md` - System automatically creates missing `systempatterns.md` and `techcontext.md` if they don't exist\n3. Update `activecontext.md` - System can automatically update `progress.md` based on changes\n4. Search for \"authentication\" - System searches across all project files and finds relevant content\n\n## Architecture\n\n### Storage\n- All memories stored in MongoDB collections\n- Each project gets isolated storage\n- Automatic indexing for fast text search\n- Optional vector embeddings for semantic search (Atlas only)\n\n### Performance\n- Text search: ~50-200ms for thousands of documents\n- Memory retrieval: ~10-50ms per document\n- Semantic search: ~50-150ms (Atlas with vector search)\n- Concurrent access supported\n\n### Data Structure\nEach memory document contains:\n- Project name and file name\n- Content and metadata (word count, timestamps)\n- Auto-generated tags\n- Template type and relationships (if applicable)\n- Vector embeddings (Atlas only)\n\n## Why MongoDB Instead of Files?\n\n### Performance\n- Fast indexed search instead of scanning files\n- Concurrent access without file locking issues\n- Efficient storage and retrieval at scale\n- Rich query capabilities beyond simple text matching\n\n### Reliability\n- ACID transactions ensure data consistency\n- Automatic backup and replication (Atlas)\n- No file corruption or permission issues\n- Built-in connection pooling and error handling\n\n### Features\n- Text search with relevance scoring\n- Semantic search with vector embeddings (Atlas)\n- Complex queries and aggregations\n- Real-time indexing and updates\n\n## Use Cases\n\n### Development Documentation\n- Project requirements and architecture decisions\n- Code patterns and implementation notes\n- Bug fixes and troubleshooting guides\n- API documentation and integration patterns\n\n### Knowledge Management\n- Meeting notes and team decisions\n- Research findings and technical insights\n- Learning notes and skill development\n- Best practices and coding standards\n\n### AI Assistant Memory\n- Persistent context across sessions\n- Project-specific knowledge retention\n- Automatic organization and tagging\n- Intelligent content relationships\n\n## Security\n\n- Input validation and sanitization on all operations\n- Path security validation to prevent directory traversal\n- MongoDB injection prevention\n- Secure connection handling with proper error management\n\n## Requirements\n\n- Node.js 18+\n- MongoDB Community Edition 4.4+ or MongoDB Atlas\n- For semantic search: MongoDB Atlas with vector search enabled\n- For embeddings: Voyage AI API key\n\n## Contributing\n\n1. Fork the repository\n2. Create a feature branch\n3. Add tests for new features\n4. Ensure all tests pass\n5. Submit a pull request\n\n## License\n\nMIT License - see [LICENSE](LICENSE) file for details.\n\n## Dependencies\n\n- **MongoDB**: Database backend\n- **Voyage AI**: Vector embeddings for semantic search (Atlas only)\n- **Model Context Protocol**: AI tool integration standard\n","readmeFilename":"README.md"}