mcp-lance-db

# mcp-lance-db: A LanceDB MCP server > The [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you're building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need. This repository is an example of how to create a MCP server for [LanceDB](https://lancedb.com/), an embedded vector database. ## Overview A basic Model Context Protocol server for storing and retrieving memories in the LanceDB vector database. It acts as a semantic memory layer that allows storing text with vector embeddings for later retrieval. ## Components ### Tools The server implements two tools: - add-memory: Adds a new memory to the vector database - Takes "content" as a required string argument - Stores the text with vector embeddings for later retrieval - search-memories: Retrieves semantically similar memories - Takes "query" as a required string argument - Optional "limit" parameter to control number of results (default: 5) - Returns memories ranked by semantic similarity to the query - Updates server state and notifies clients of resource changes ## Configuration The server uses the following configuration: - Database path: "./lancedb" - Collection name: "memories" - Embedding provider: "sentence-transformers" - Model: "BAAI/bge-small-en-v1.5" - Device: "cpu" - Similarity threshold: 0.7 (upper bound for distance range) ## Quickstart #### Claude Desktop On MacOS: `~/Library/Application\ Support/Claude/claude_desktop_config.json` On Windows: `%APPDATA%/Claude/claude_desktop_config.json` ``` { "lancedb": { "command": "uvx", "args": [ "mcp-lance-db" ] } } ``` ## Development ### Building and Publishing To prepare the package for distribution: 1. Sync dependencies and update lockfile: ```bash uv sy…