What is Mcp Server Lancedb

mcp-server-lancedb is a Model Context Protocol (MCP) server designed for storing and retrieving memories in LanceDB, an embedded vector database. It serves as a semantic memory layer that allows the storage of text with vector embeddings for later retrieval.

Use cases

Use cases for mcp-server-lancedb include building AI-powered IDEs, enhancing chat interfaces, and creating custom AI workflows that require contextual memory storage and retrieval.

How to use

To use mcp-server-lancedb, configure the server by setting the database path, collection name, embedding provider, model, and device. Use the ‘add-memory’ tool to add new memories and the ‘search-memories’ tool to retrieve semantically similar memories based on a query.

Key features

Key features include the ability to add and retrieve memories with vector embeddings, support for semantic similarity searches, and a configurable environment for database settings and embedding models.

Where to use

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mcp-lance-db: A LanceDB MCP server

The Model Context Protocol (MCP) 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, 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:

uv sync

2. Build package distributions:

uv build

This will create source and wheel distributions in the dist/ directory.

3. Publish to PyPI:

uv publish

Note: You’ll need to set PyPI credentials via environment variables or command flags:

• Token: --token or UV_PUBLISH_TOKEN
• Or username/password: --username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORD

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging
experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

npx @modelcontextprotocol/inspector uv --directory $(PWD) run mcp-lance-db

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.