lance-mcp

lance-mcp

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LanceDB MCP Server for LLMS is designed to enable large language models to dynamically search and interact with locally stored documents through LanceDB. It offers efficient token usage and ensures data security by storing the index locally.

🗄️ LanceDB MCP Server for LLMS

Node.js 18+ License: MIT

A Model Context Protocol (MCP) server that enables LLMs to interact directly the documents that they have on-disk through agentic RAG and hybrid search in LanceDB. Ask LLMs questions about the dataset as a whole or about specific documents.

✨ Features

  • 🔍 LanceDB-powered serverless vector index and document summary catalog.
  • 📊 Efficient use of LLM tokens. The LLM itself looks up what it needs when it needs.
  • 📈 Security. The index is stored locally so no data is transferred to the Cloud when using a local LLM.

🚀 Quick Start

To get started, create a local directory to store the index and add this configuration to your Claude Desktop config file:

MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "lancedb": {
      "command": "npx",
      "args": [
        "lance-mcp",
        "PATH_TO_LOCAL_INDEX_DIR"
      ]
    }
  }
}

Prerequisites

  • Node.js 18+
  • npx
  • MCP Client (Claude Desktop App for example)
  • Summarization and embedding models installed (see config.ts - by default we use Ollama models)
    • ollama pull snowflake-arctic-embed2
    • ollama pull llama3.1:8b

Demo

Local Development Mode:
{
  "mcpServers": {
    "lancedb": {
      "command": "node",
      "args": [
        "PATH_TO_LANCE_MCP/dist/index.js",
        "PATH_TO_LOCAL_INDEX_DIR"
      ]
    }
  }
}

Use npm run build to build the project.

Use npx @modelcontextprotocol/inspector dist/index.js PATH_TO_LOCAL_INDEX_DIR to run the MCP tool inspector.

Seed Data

The seed script creates two tables in LanceDB - one for the catalog of document summaries, and another one - for vectorized documents' chunks. To run the seed script use the following command:

npm run seed -- --dbpath <PATH_TO_LOCAL_INDEX_DIR> --filesdir <PATH_TO_DOCS>

You can use sample data from the docs/ directory. Feel free to adjust the default summarization and embedding models in the config.ts file. If you need to recreate the index, simply rerun the seed script with the --overwrite option.

Catalog
  • Document summary
  • Metadata
Chunks
  • Vectorized document chunk
  • Metadata

🎯 Example Prompts

Try these prompts with Claude to explore the functionality:

"What documents do we have in the catalog?"
"Why is the US healthcare system so broken?"

📝 Available Tools

The server provides these tools for interaction with the index:

Catalog Tools

  • catalog_search: Search for relevant documents in the catalog

Chunks Tools

  • chunks_search: Find relevant chunks based on a specific document from the catalog
  • all_chunks_search: Find relevant chunks from all known documents

📜 License

This project is licensed under the MIT License - see the file for details.