MCP server for vector search using LanceDB
Mcp lancedb node mcp server lets Claude, Cursor and other MCP clients work with MCP LanceDB Node directly. MCP server for vector search using LanceDB.
A Node.js implementation for vector search using LanceDB and Ollama's embedding model.
This project demonstrates how to: - Connect to a LanceDB database - Create custom embedding functions using Ollama - Perform vector similarity search against stored documents - Process and display search results
Setup follows the standard MCP pattern: clone or install the server, then register it in your client's configuration file and restart the client. The configuration snippets on this page cover Claude Desktop, Claude Code and Cursor.
nomic-embed-text model - LanceDB storage location with read/write permissionsDeveloper-tool servers are usually the first ones people connect, because they turn "help me with this code" into an assistant that can actually read the repo and act on it. MCP LanceDB Node sits in that group. Worth comparing against the other developer tools servers in this directory before you commit to one, since several overlap in scope but differ sharply in setup cost and permissions.
{
"mcpServers": {
"lanceDB": {
"command": "node",
"args": [
"/path/to/lancedb-node/dist/index.js",
"--db-path",
"/path/to/your/lancedb/storage"
]
}
}
}Configuration as documented by the project. Restart the client after saving.
nomic-embed-text model - LanceDB storage location with read/write permissionsBuild a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.