How do I use LanceDB MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type `@` followed by the MCP server name and your instructions, e.g., "`@LanceDB MCP Server` search for similar vectors to this query in my_table" That's it! The server will respond to your query, and you can continue using it as needed. Here is a [step-by-step guide with screenshots](https://glama.ai/blog/2025-07-08-how-to-install-and-use-mcp-servers).

LanceDB MCP Server

Overview

A Model Context Protocol (MCP) server implementation for LanceDB vector database operations. This server enables efficient vector storage, similarity search, and management of vector embeddings with associated metadata.

Related MCP server: Chroma MCP Server

Components

Resources

The server exposes vector database tables as resources:

table://{name}: A vector database table that stores embeddings and metadata
- Configurable vector dimensions
- Text metadata support
- Efficient similarity search capabilities

API Endpoints

Table Management

POST /table
- Create a new vector table
- Input:
  { "name": "my_table", # Table name "dimension": 768 # Vector dimension }

Vector Operations

POST /table/{table_name}/vector
- Add vector data to a table
- Input:
  { "vector": [0.1, 0.2, ...], # Vector data "text": "associated text" # Metadata }
POST /table/{table_name}/search
- Search for similar vectors
- Input:
  { "vector": [0.1, 0.2, ...], # Query vector "limit": 10 # Number of results }

Installation

# Clone the repository git clone https://github.com/yourusername/lancedb_mcp.git cd lancedb_mcp # Install dependencies using uv uv pip install -e .

Usage with Claude Desktop

# Add the server to your claude_desktop_config.json "mcpServers": { "lancedb": { "command": "uv", "args": [ "run", "python", "-m", "lancedb_mcp", "--db-path", "~/.lancedb" ] } }