# Qdrant

> Use this tool when you need to implement semantic search capabilities with complete data privacy, enabling natural language search and metadata filtering for building private knowledge bases, document search systems, and AI assistants. It takes in text documents and outputs searchable embeddings, utilizing OpenAI models and a local Qdrant vector database. Ideal for use cases requiring private and secure semantic search, such as document management and AI-powered information retrieval systems.

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

## Description

Qdrant MCP Server provides semantic search capabilities using a local Qdrant vector database and OpenAI embeddings, built by mhalder with comprehensive TypeScript implementation and 114 unit tests. The server automatically converts text documents to embeddings using OpenAI's models, stores them in a locally-running Qdrant instance via Docker for complete data privacy, and enables natural language search with metadata filtering using Qdrant's powerful filter syntax. It supports full collection lifecycle management (create, delete, info), document operations with automatic UUID normalization for string IDs, and both simple key-value and complex boolean filter expressions, making it valuable for building private knowledge bases, document search systems, and AI assistants that need semantic search without sending data to external vector database services.

## Trust

- **Trust score (0–1):** 0.86
- **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/mhalder-qdrant)
- **Repository:** <https://github.com/mhalder/qdrant-mcp-server>

## 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": "mhalder-qdrant"
    }
  }
}
```

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