# mcp_qrant

> Use this tool when you need to efficiently store and query large vector databases, and solve problems related to semantic search and self-embedding. It provides a unified interface, currently supporting Qdrant, to streamline your workflow and improve search accuracy. With mcp_qrant, you can input vector data and output relevant search results, making it ideal for applications requiring intelligent information retrieval.

Canonical page: https://skillsregistry.net/skills/didierphmartin-mcp-qrant  
JSON: https://api.skillsregistry.net/v1/skills/didierphmartin-mcp-qrant

## Description

Provides a unified interface for storing and querying vector databases, currently supporting Qdrant with self-embedding and semantic search.

## Trust

- **Trust score (0–1):** 0.68
- **Verification tier:** scanned
- **Last scanned:** 2026-08-30

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-08-30

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/o6lijzdb9m)
- **Repository:** <https://github.com/didierphmartin/mcp_qrant>

## 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": "didierphmartin-mcp-qrant"
    }
  }
}
```

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