# Qdrant Vector Database

> Use this tool when you need to store and retrieve information using semantic search capabilities, solving problems of inefficient keyword-based searches and enabling AI assistants to find relevant data based on meaning. The Qdrant Vector Database takes in text and optional metadata as input and outputs relevant search results, with support for multiple embedding providers. It is ideal for use cases requiring persistent memory storage with efficient retrieval, such as chatbots and virtual assistants.

Canonical page: https://skillsregistry.net/skills/jimmy974-qdrant  
JSON: https://api.skillsregistry.net/v1/skills/jimmy974-qdrant

## Description

MCP Server for Qdrant provides a vector database integration for storing and retrieving information using semantic search capabilities. Built with Python, it supports multiple embedding providers including FastEmbed, sentence-transformers, and lightweight alternatives optimized for Alpine Linux environments with minimal dependencies. The server offers two main tools: 'qdrant-store' for saving text with optional metadata and 'qdrant-find' for semantic searching of stored information. It can be deployed via Docker or run locally, making it ideal for AI assistants that need persistent memory storage with efficient retrieval based on meaning rather than exact keyword matching.

## Trust

- **Trust score (0–1):** 0.99
- **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:** [PulseMCP](https://www.pulsemcp.com/servers/jimmy974-qdrant)
- **Repository:** <https://github.com/jimmy974/mcp-server-qdrant>

## 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": "jimmy974-qdrant"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/jimmy974-qdrant` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/jimmy974-qdrant/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