mcp-lancedb

# 🗄️ LanceDB MCP Server for LLMS [](https://nodejs.org/en/) [](https://opensource.org/licenses/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` ```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 <img src="https://github.com/user-attachments/assets/90bfdea9-9edd-4cf6-bb04-94c9c84e4825" width="50%"> #### Local Development Mode: ```json { "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: ```console 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: ```plaintext "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](LICENSE) file for details.