# RAG Documentation

> Use this tool when you need to enhance AI responses with relevant documentation context, solve problems related to semantic documentation retrieval, and automate knowledge base augmentation. It takes in natural language queries and outputs context-aware documentation access, leveraging vector search and queue management. Ideal for developers building documentation-aware AI systems, such as context-enhanced chatbots and semantic documentation search.

Canonical page: https://skillsregistry.net/skills/rahulretnan-rag-documentation  
JSON: https://api.skillsregistry.net/v1/skills/rahulretnan-rag-documentation

## Description

This RAG documentation MCP server, developed by Rahul Retnan as a fork of qpd-v's original project, enables AI assistants to augment their responses with relevant documentation context. Built with TypeScript and integrating Qdrant for vector search, it offers tools for semantic documentation retrieval, source management, and automated processing of new content. The implementation focuses on enhancing AI capabilities through context-aware documentation access, with features like natural language querying and efficient queue management. It's particularly useful for developers building documentation-aware AI systems, enabling use cases such as context-enhanced chatbots, semantic documentation search, and automated knowledge base augmentation without directly handling vector database complexities.

## Trust

- **Trust score (0–1):** 0.42
- **Verification tier:** scanned
- **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/rahulretnan-rag-documentation)
- **Repository:** <https://github.com/rahulretnan/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": "rahulretnan-rag-documentation"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/rahulretnan-rag-documentation` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/rahulretnan-rag-documentation/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