MCP Servers

LanceDB MCP Server

Enables efficient vector database operations for embedding storage and similarity search through a Model Context Protocol interface.

RyanLisse

Databases

Knowledge & Memory

Local

Python

README

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.

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.

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