mongodocs-mcp

MongoDB Semantic MCP v7.0

Lightning-fast semantic search for MongoDB and Voyage AI documentation via Model Context Protocol. Search 10,000+ documents in <500ms with the latest AI models.

🚀 Features

• Smart Model Selection: Uses voyage-code-3 for code content and voyage-3 for text
• Blazing Fast: <500ms search across 10,000+ documents
• High Accuracy: 96%+ success rate on all query types
• Instruction-Following Reranker: Uses rerank-2.5 for superior relevance
• MongoDB Native: Optimized for MongoDB Atlas Vector Search
• Beautiful CLI: Progress bars, colors, and interactive setup

📦 Installation

npm install -g mongodocs-mcp

🔧 Setup

Prerequisites

• MongoDB Atlas cluster with Vector Search enabled
• Voyage AI API key

Quick Start

# 1. Install globally
npm install -g mongodocs-mcp

# 2. Create .env file
cat > .env << EOF
MONGODB_URI=mongodb+srv://user:[e-mail]/
MONGODB_DATABASE=mongodb_semantic_docs
MONGODB_COLLECTION=documents
VOYAGE_API_KEY=pa-your-voyage-api-key
EOF

# 3. Check status (optional)
mongodocs-status

# 4. Index documentation (10-30 minutes)
mongodocs-index

# 5. Start MCP server
mongodocs-mcp

Environment Variables

Variable	Description	Default
MONGODB_URI	MongoDB Atlas connection string	Required
MONGODB_DATABASE	Database name	mongodb_semantic_docs
MONGODB_COLLECTION	Collection name	documents
VOYAGE_API_KEY	Voyage AI API key for embeddings	Required

Troubleshooting

# Check if everything is set up correctly
mongodocs-status

# Get help for any command
mongodocs-mcp --help
mongodocs-index --help
mongodocs-status --help

🎯 Using with Cursor IDE

Add to your ~/.cursor/mcp.json:

{
  "mcpServers": {
    "mongodocs-mcp": {
      "command": "npx",
      "args": ["mongodocs-mcp@latest"]
    }
  }
}

📊 MCP Tools

The MCP provides these tools to AI agents:

• mongodb-semantic-search - Search documentation with natural language
• mongodb-find-similar - Find similar documents to provided content
• mongodb-explain-concept - Get comprehensive explanations
• mongodb-refresh-docs - Update documentation database
• mongodb-status - Check system status

🧠 Technical Details

Embedding Models

• voyage-3: General documentation (60-70% of content)
• voyage-code-3: Code examples and API references (30-40% of content)
• rerank-2.5: Instruction-following reranker for result optimization

Performance

• Index size: ~10,380 documents
• Search latency: <500ms average
• Accuracy: 96%+ query success rate

MongoDB Configuration

• Database: mongodb_semantic_docs
• Collection: documents
• Index: semantic_search (vector search index)

🛠️ CLI Tools

mongodocs-index

Indexes all MongoDB and Voyage AI documentation. Features:

• Smart model selection (voyage-3 vs voyage-code-3)
• Progress tracking with beautiful UI
• Incremental updates
• Content hashing for efficiency

mongodocs-clean

Removes all indexed documents for a fresh start.

mongodocs-setup

Interactive setup wizard for configuration.

📈 Version History

v7.0.0 (Current)

• Added voyage-code-3 for code content
• Smart content detection
• Model distribution tracking
• 13.80% better code search accuracy

v6.0.0

• Upgraded to rerank-2.5
• Instruction-following for better results
• Special character handling improvements

🤝 Contributing

Contributions welcome! Please ensure:

• Code passes TypeScript compilation
• All queries maintain <500ms latency
• Test coverage for new features

📄 License

MIT

🔗 Links

• MongoDB Atlas
• Voyage AI
• Model Context Protocol

Built with ❤️ for the MongoDB community