MCP-Lance-DB: Lightning Speed & Seamless Scalability

Key Features of MCP-Lance-DB: Lightning Speed & Seamless Scalability?

• Rapid Vector Operations: Leverages LanceDB’s optimized storage for low-latency embedding storage and search.
• Adaptive Semantic Search: Uses BAAI’s BGE model to rank results by contextual relevance, with a customizable similarity threshold.
• Deployment Flexibility: Supports configuration tweaks for embedding models, database paths, and scaling options.
• Debugging Integration: Seamlessly pairs with the MCP Inspector tool for real-time performance analysis.

Use Cases of MCP-Lance-DB: Lightning Speed & Seamless Scalability?

Primarily designed for:

• AI assistants requiring context-aware dialogue management.
• Data analysis tools needing quick access to stored knowledge bases.
• IDE plugins that enhance code suggestions with historical project data.
• Chatbots that adapt responses based on past interactions.

FAQ: Getting the Most from MCP-Lance-DB

How do I adjust the semantic similarity threshold?

Edit the configuration parameters to set the similarity_threshold value before initialization.

Can I use a different embedding model?

Yes, replace the default BGE model by specifying a supported HuggingFace model in your setup.

What platforms are officially supported?

Works natively with Linux/macOS environments and integrates with cloud services via Docker deployments.

Why use LanceDB over other vector databases?

LanceDB’s columnar storage and GPU acceleration provide faster write speeds and query performance for large datasets.

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.