# RAG Document Server

> Use this tool when you need to semantically search and answer questions over large document collections, solving problems like information retrieval and knowledge discovery. It takes in uploaded documents and outputs relevant answers and search results, using vector embeddings and Google AI to power its query capabilities. Ideal for use cases where complex document organization and section-aware querying are required.

Canonical page: https://skillsregistry.net/skills/jaimeferj-mcp-rag-docs  
JSON: https://api.skillsregistry.net/v1/skills/jaimeferj-mcp-rag-docs

## Description

Enables semantic search and question-answering over uploaded documents using vector embeddings and Google AI. Supports document organization with tags, section-aware queries, and hierarchical markdown structure preservation.

## Trust

- **Trust score (0–1):** 0.63
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-28

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/xz97rokby3)
- **Repository:** <https://github.com/jaimeferj/mcp-rag-docs>

## 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": "jaimeferj-mcp-rag-docs"
    }
  }
}
```

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