mcp-lance-db: A LanceDB MCP server
@kyryl-opens-ml
About mcp-lance-db: A LanceDB MCP server
No overview available yet
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp-server-lancedb": {
"command": "uv",
"args": [
"sync"
]
}
}
}Tools
2Adds a new memory to the vector database
Retrieves semantically similar memories
Overview
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
FAQ from mcp-lance-db
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.
Frequently asked questions
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`.
Basic information
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