# LanceDB

> Use this tool when you need to efficiently store and search vector embeddings with associated metadata for AI-assisted workflows, such as semantic search, recommendation systems, or similarity lookups on high-dimensional data. LanceDB provides a vector database interface with key operations like table creation, vector addition, and nearest neighbor searches. It integrates with MCP clients, enabling natural language interactions with vector data through a Python-based API.

Canonical page: https://skillsregistry.net/skills/ryanlisse-lancedb  
JSON: https://api.skillsregistry.net/v1/skills/ryanlisse-lancedb

## Description

This LanceDB MCP server, developed by Ryan Lisse, provides a vector database interface for AI-assisted workflows. Built with Python and leveraging FastAPI, it enables efficient storage and similarity search of vector embeddings with associated metadata. The implementation supports key operations like creating tables, adding vectors, and performing nearest neighbor searches. It integrates seamlessly with Claude Desktop and other MCP clients, allowing natural language interactions with vector data. This server is particularly useful for developers and data scientists working on applications involving semantic search, recommendation systems, or any task requiring fast similarity lookups on high-dimensional data.

## Trust

- **Trust score (0–1):** 0.96
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-09-19

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/ryanlisse-lancedb)
- **Repository:** <https://github.com/ryanlisse/lancedb_mcp>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "ryanlisse-lancedb"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/ryanlisse-lancedb` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/ryanlisse-lancedb/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt