# MCP RAG System

> Use this tool when you need to semantically search and generate answers from large collections of PDF documents. The MCP RAG System solves problems of information retrieval and question answering by leveraging vector embeddings and FAISS indexing to provide context-aware responses. It takes PDF documents as input and outputs relevant answers to user queries, making it ideal for applications requiring efficient document analysis and knowledge extraction.

Canonical page: https://skillsregistry.net/skills/nitin-kumar101-mcp  
JSON: https://api.skillsregistry.net/v1/skills/nitin-kumar101-mcp

## Description

A Retrieval-Augmented Generation system that enables uploading, processing, and semantic search of PDF documents using vector embeddings and FAISS indexing for context-aware question answering.

## Trust

- **Trust score (0–1):** 0.92
- **Verification tier:** verified
- **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/x1axxenxvb)
- **Repository:** <https://github.com/nitin-kumar101/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": "nitin-kumar101-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/nitin-kumar101-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/nitin-kumar101-mcp/pull`

---
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