# RAG Documentation Search

> Use this tool when you need to augment AI responses with relevant documentation context, enabling semantic document search and retrieval. It solves problems of context-aware information retrieval, supporting both local and cloud-based embeddings generation. The tool takes in documentation sources as input and outputs relevant context to inform AI assistant responses, ideal for workflows requiring accurate and informed AI interactions.

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

## Description

The MCP-server-ragdocs provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context. Built with TypeScript, it supports both local (Ollama) and cloud-based (OpenAI) embeddings generation, integrates with Qdrant for vector storage, and includes tools for semantic document search, URL extraction, and queue management. This implementation is particularly valuable for workflows requiring context-aware AI responses backed by specific documentation sources.

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-04-25

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/sanderkooger-ragdocs)
- **Repository:** <https://github.com/sanderkooger/mcp-server-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": "sanderkooger-ragdocs"
    }
  }
}
```

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