# RAG Document Server

> Use this tool when you need to efficiently process and retrieve documents, enabling AI agents to search and analyze text without relying on large language models. The RAG Document Server solves document processing and retrieval challenges by providing a deterministic MCP server for chunking and vector-searching documents. It accepts document inputs and outputs processed and indexed data for AI agents to utilize in various applications.

Canonical page: https://skillsregistry.net/skills/vamshi9415-docrag-mcp  
JSON: https://api.skillsregistry.net/v1/skills/vamshi9415-docrag-mcp

## Description

A deterministic MCP server for document processing and retrieval that enables AI agents to process, chunk, and vector-search documents without an LLM.

## Trust

- **Trust score (0–1):** 0.64
- **Verification tier:** scanned
- **Last scanned:** 2026-08-31

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-08-31

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/dmdgdvuekj)
- **Repository:** <https://github.com/Vamshi9415/docrag-mcp>

## 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": "vamshi9415-docrag-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/vamshi9415-docrag-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/vamshi9415-docrag-mcp/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