# LanceDB MCP Server

> Use this tool when you need to efficiently store and search embeddings in a vector database, solving problems like similarity search and nearest neighbor queries. It provides a Model Context Protocol (MCP) interface for seamless interactions, accepting embedding data as input and returning similarity search results as output. Ideal for applications requiring fast and accurate vector similarity searches, such as image and text retrieval systems.

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

## Description

Enables efficient vector database operations for embedding storage and similarity search through a Model Context Protocol interface.

## Trust

- **Trust score (0–1):** 0.96
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-09-28

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/a34mk639ba)
- **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-mcp"
    }
  }
}
```

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