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mcp-lancedb

mcp-lancedb

Public
adiom-data/lance-mcp

Enables LLMs to interact with on-disk documents via Model Context Protocol, offering serverless vector indexing, document summarization, hybrid search, and secure local data handling for efficient and private dataset querying.

typescript
0 tools
May 29, 2025
Updated Jun 4, 2025

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🗄️ LanceDB MCP Server for LLMS

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 --filesdir

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 LICENSE file for details.

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