# RAG Docs

> Use this tool when you need to perform semantic search and retrieval of documentation, enabling AI assistants to quickly find relevant information within large document collections. It solves problems related to context-aware information retrieval, knowledge base augmentation, and efficient access to domain-specific documentation. The tool accepts URLs and text queries as inputs and returns relevant documentation and source listings as outputs.

Canonical page: https://skillsregistry.net/skills/qpd-v-ragdocs  
JSON: https://api.skillsregistry.net/v1/skills/qpd-v-ragdocs

## Description

This MCP server, developed by qpd-v, enables AI assistants to perform semantic search and retrieval of documentation using a vector database (Qdrant). It provides tools for adding documentation from URLs, searching through stored content, and listing sources. The server implements web scraping, text chunking, and embedding generation using either Ollama or OpenAI. By connecting AI capabilities with vector search technology, this implementation empowers AI assistants to quickly find relevant information within large document collections. It is particularly useful for applications requiring context-aware information retrieval, knowledge base augmentation, or any scenario where an AI system needs to efficiently access and reason about domain-specific documentation.

## 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/qpd-v-ragdocs)
- **Repository:** <https://github.com/qpd-v/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": "qpd-v-ragdocs"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/qpd-v-ragdocs` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/qpd-v-ragdocs/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