# Qdrant-MCP

> Qdrant-MCP — ncasfl-qdrant-mcp. Use this tool when you need to leverage a vector database and GPU-accelerated embedding pipeline for AI model development, such as Claude Code, Claude.ai, and OpenClaw, to efficiently store and process vector embeddings. It solves problems related to scalable and performant embedding management, providing a CP server interface for seamless integration. Ideal for use cases requiring fast and accurate embedding processing, such as natural language processing and machine learning model training.

Canonical page: https://skillsregistry.net/skills/ncasfl-qdrant-mcp  
JSON: https://api.skillsregistry.net/v1/skills/ncasfl-qdrant-mcp

## Description

CP server exposing Qdrant vector DB store and GPU-accelerated embedding pipeline as tools for Claude Code, Claude.ai, and OpenClaw.

## Trust

- **Trust score (0–1):** 0.91
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** MIT
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/ncasfl/Qdrant-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": "ncasfl-qdrant-mcp"
    }
  }
}
```

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