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: KGrag 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"
    ]
  }
}

Development

# Install development dependencies
uv pip install -e ".[dev]"

# Run tests
pytest

# Format code
black .
ruff .

Environment Variables

• LANCEDB_URI: Path to LanceDB storage (default: ".lancedb")

License

This project is licensed under the MIT License. See the LICENSE file for details.