# nist-rag-mcp-server

> nist-rag-mcp-server — nourawada02-multiagent-mcp-project. Use this tool when you need to perform grounded question answering over NIST AI RMF documents, leveraging local RAG with selective OCR and hybrid retrieval. It solves problems related to information retrieval and document analysis, providing verified visual figure retrieval and access to a read-only resource catalog. The tool exposes two main interfaces, ask_nist_rag and get_nist_visual, for querying and retrieving relevant information.

Canonical page: https://skillsregistry.net/skills/nourawada02-multiagent-mcp-project  
JSON: https://api.skillsregistry.net/v1/skills/nourawada02-multiagent-mcp-project

## Description

A local FastMCP STDIO server exposing two tools (ask_nist_rag and get_nist_visual) and one read-only resource (nist://visuals/catalog) that provides grounded question answering over NIST AI RMF documents using local RAG with selective OCR, hybrid retrieval, and verified visual figure retrieval.

## Trust

- **Trust score (0–1):** 0.65
- **Verification tier:** scanned
- **Last scanned:** 2026-08-29

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-08-29

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/szoac6rk1j)
- **Repository:** <https://github.com/nourawada02/MultiAgent_MCP_Project>

## 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": "nourawada02-multiagent-mcp-project"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/nourawada02-multiagent-mcp-project` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/nourawada02-multiagent-mcp-project/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