# Agentic RAG MCP

> Use this tool when you need to generate well-researched and cited answers to complex questions, leveraging a knowledge base and live web research to provide accurate and reliable information. It solves problems of information retrieval, evidence-based reasoning, and answer validation, making it ideal for applications requiring trustworthy and transparent responses. The tool takes natural language questions as input and produces cited answers with supporting evidence as output.

Canonical page: https://skillsregistry.net/skills/enached134-ctrl-agentic-rag-mcp  
JSON: https://api.skillsregistry.net/v1/skills/enached134-ctrl-agentic-rag-mcp

## Description

A multi-agent Retrieval-Augmented Generation system exposed as an MCP server. Ask a question and a LangGraph pipeline plans the retrieval, pulls evidence from a pgvector knowledge base, optionally augments it with live web research, drafts a cited answer, and then self-critiques it for grounding — revising until the answer is supported by the sources.

## Trust

- **Trust score (0–1):** 0.69
- **Verification tier:** scanned
- **Last scanned:** 2026-08-30

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** devops-ci
- **Updated:** 2026-08-30

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/kms8lfb4hs)
- **Repository:** <https://github.com/enached134-ctrl/agentic-rag-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": "enached134-ctrl-agentic-rag-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/enached134-ctrl-agentic-rag-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/enached134-ctrl-agentic-rag-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