# Qdrant Vector Database

> Use this tool when you need to store and retrieve technical knowledge and code snippets using semantic queries, enabling AI assistants to maintain a persistent memory of information across conversations. It solves problems of knowledge management and retrieval, allowing for efficient searching of code examples and technical information using natural language inputs. The Qdrant Vector Database takes in natural language descriptions and code snippets as inputs and outputs relevant search results, making it ideal for use cases requiring advanced knowledge retrieval capabilities.

Canonical page: https://skillsregistry.net/skills/changjunpark-qdrant-vector-database  
JSON: https://api.skillsregistry.net/v1/skills/changjunpark-qdrant-vector-database

## Description

MCP-Qdrant Server provides a vector database integration for AI assistants, combining a Qdrant vector database with a specialized server that enables knowledge storage and retrieval. The implementation uses Docker containers to run both the Qdrant database and the MCP server, with the server utilizing the sentence-transformers embedding model to convert natural language into vector representations. It exposes two primary tools: one for storing code snippets with natural language descriptions, and another for searching the knowledge base using semantic queries. This setup is particularly useful for AI assistants that need to maintain persistent memory of code examples and technical knowledge across conversations.

## Trust

- **Trust score (0–1):** 1.00
- **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/changjunpark-qdrant-vector-database)
- **Repository:** <https://github.com/changjunpark/mcp-qdrant-server-with-qdrant-db>

## 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": "changjunpark-qdrant-vector-database"
    }
  }
}
```

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