mcp-lance-db: A LanceDB MCP server

@kyryl-opens-ml

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基本情報

カテゴリ

データベース

トランスポート

stdio

作者

kyryl-opens-ml

mcp-lance-db: A LanceDB MCP server について

概要はまだありません

#mcp-server #lancedb #mcp

mcp-lance-db: A LanceDB MCP server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "mcp-server-lancedb": {
      "command": "uv",
      "args": [
        "sync"
      ]
    }
  }
}

mcp-lance-db: A LanceDB MCP server のツール

Adds a new memory to the vector database

Retrieves semantically similar memories

概要

What is mcp-lance-db?

mcp-lance-db is a Model Context Protocol server that stores and retrieves memories in the LanceDB vector database. It acts as a semantic memory layer for LLM applications, allowing text to be stored with vector embeddings for later retrieval.

How to use mcp-lance-db?

Configure it in your MCP client (e.g., Claude Desktop) by adding the command uvx mcp-lance-db to the client’s config file. For Claude Desktop, the config file is claude_desktop_config.json. The server then exposes two tools: add-memory and search-memories.

Key features of mcp-lance-db

Adds new text memories with vector embeddings
Searches memories by semantic similarity
Configurable result limit (default 5)
Embeds using sentence-transformers (BAAI/bge-small-en-v1.5)
Stores data locally in a LanceDB database
Notifies clients of resource changes

Use cases of mcp-lance-db

Provide a persistent semantic memory for conversational AI assistants
Enable retrieval of past context or facts by meaning, not keywords
Build a lightweight personal knowledge base for LLM tools
Store and recall user preferences or session history semantically

よくある質問

What does mcp-lance-db do?

It is a basic MCP server that stores text as vector embeddings in LanceDB and retrieves memories that are semantically similar to a query.

What tools does mcp-lance-db provide?

It provides two tools: `add-memory` (takes a required `content` string) and `search-memories` (takes a required `query` string and optional `limit` parameter).

What are the default configuration values?

Database path is `./lancedb`, collection name is `memories`, embedding model is `BAAI/bge-small-en-v1.5` on CPU, and the similarity threshold is 0.7.

How do I run mcp-lance-db for debugging?

Use the MCP Inspector: run `npx @modelcontextprotocol/inspector uv --directory $(PWD) run mcp-lance-db` and open the provided URL in a browser.

How is mcp-lance-db installed?

It is distributed as a Python package and can be run via `uvx mcp-lance-db`. No manual installation is needed if using `uv`.

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