# Qdrant Docs Rag

> Use this tool when you need to augment AI responses with relevant documentation context or perform semantic searches on technical information. It solves problems such as knowledge gaps in AI assistants and inefficient documentation searches by providing vector-based search and retrieval capabilities. The tool takes in URLs and documentation as input and outputs relevant context and information, making it ideal for building documentation-aware AI assistants and developer tools.

Canonical page: https://skillsregistry.net/skills/hannesrudolph-qdrant-docs-rag  
JSON: https://api.skillsregistry.net/v1/skills/hannesrudolph-qdrant-docs-rag

## Description

This MCP server, developed by Hannes Rudolph, enables AI assistants to augment their responses with relevant documentation context through vector-based search and retrieval. Built as a fork of qpd-v's original implementation, it integrates with OpenAI for embeddings generation and Qdrant for vector storage. The server provides tools for adding documentation from URLs, performing semantic searches, extracting links, and managing a processing queue. By connecting AI capabilities with efficient vector search of documentation, this implementation allows AI systems to enhance their knowledge with domain-specific information in real-time. It is particularly useful for building documentation-aware AI assistants, implementing semantic documentation search, and creating context-aware developer tools that require access to up-to-date technical information.

## Trust

- **Trust score (0–1):** 0.95
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-19

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/hannesrudolph-qdrant-docs-rag)
- **Repository:** <https://github.com/hannesrudolph/mcp-ragdocs>

## 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": "hannesrudolph-qdrant-docs-rag"
    }
  }
}
```

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