---
type: entity
subtype: tool
title: "Crawl4AI RAG MCP Server"
description: "Cole's open-source MCP server that gives coding assistants external knowledge via RAG and now knowledge-graph hallucination detection."
resource: "https://github.com/coleam00/mcp-crawl4ai-rag"
tags: [mcp, rag, knowledge-graphs, open-source]
videos: [ai-code-that-fixes-itself-an-mcp-you-can-try-now, all-the-best-rag-strategies-in-one-mcp-for-ai-coding-assistants]
created: 2026-07-21
updated: 2026-07-21
---

# Crawl4AI RAG MCP Server

The Crawl4AI RAG MCP Server is Cole's own open-source [MCP](../../concepts/mcp.md) server that hands a coding assistant external knowledge through [RAG](../../concepts/rag.md), and more recently through [knowledge-graph](../../concepts/knowledge-graphs.md) hallucination detection. It bundles a stack of retrieval techniques behind a single install: "it is my Crawl4AI rag MCP server. And recently, I've added in a few new rag strategies into the mix" ([0:00:17]), and it is "completely free and open source and it's very easy to set up" ([0:01:51]).

## How Cole uses it

The server exists to give AI coding assistants a curated, up-to-date knowledge source instead of relying on whatever the model memorized. It ingests documentation via [Crawl4AI](./crawl4ai.md), which handles "all of the crawling that it needs to do," embeds it, and exposes retrieval as MCP tools so assistants can pull the exact docs a task needs, a form of [codebase indexing](../../concepts/codebase-indexing.md) and [contextual retrieval](../../concepts/contextual-retrieval.md) for external libraries.

Its newest layer is a hallucination check built on a repository [knowledge graph](../../concepts/knowledge-graphs.md). Everything Cole demonstrates for catching invented method calls and wrong signatures "I have already added to my Crawl4AI rag MCP server" ([0:01:01]). The graph feature is opt-in through configuration: "if you want to use the knowledger graph part of this MCP server and your environment variables, you just have to set use knowledge graph to true" ([0:13:12]). By parsing a real codebase into [Neo4j](./neo4j.md) and validating the AI's generated code against it, the server acts as a [guardrail](../../concepts/guardrails.md) that enables [self-correction](../../concepts/self-correction.md), catching hallucinated APIs before they ship. This is the "AI code that fixes itself" loop: retrieval for grounding, plus graph-based [validation](../../concepts/validation.md) for verification, in one server.

## Realizes

- [Model Context Protocol (MCP)](../../concepts/mcp.md) - An open protocol for packaging tools, data, and RAG capabilities as servers that any AI coding assistant or agent can connect to and call with a standard interface.
- [Retrieval-Augmented Generation (RAG)](../../concepts/rag.md) - Grounding an LLM's answers by retrieving relevant documents from an external knowledge base and injecting them into the prompt at query time.
- [Knowledge Graphs](../../concepts/knowledge-graphs.md) - Structuring knowledge as entities and relationships (often temporal) so agents can traverse connections a vector store can't express.
- [Guardrails](../../concepts/guardrails.md) - Constraints and checks that keep agents from hallucinating, going off the rails, or emitting unsafe output.
- [Self-Correcting Agents](../../concepts/self-correction.md) - Feeding tool errors and results back to the model so it can reason about mistakes, avoid repeating calls, and retry until it gets the action right.
- [Validation](../../concepts/validation.md) - Building explicit checks into agentic workflows so the AI verifies its own output against real success criteria before moving on.
- [Codebase Indexing](../../concepts/codebase-indexing.md) - Building a searchable index or map of a codebase so the agent can retrieve the right files instead of scanning everything.
- [Contextual Retrieval](../../concepts/contextual-retrieval.md) - Prepending LLM-generated context to each chunk before embedding so retrieval understands where the chunk sits in the larger document.
- [llms.txt](../../concepts/llms-txt.md) - An emerging convention where a project publishes all of its documentation as a single LLM-formatted page (llms.txt / llms-full.txt), so one fetch yields complete, chunk-ready knowledge for an agent.
- [Hallucination Detection](../../concepts/hallucination-detection.md) - Deterministically checking AI-generated code against a ground-truth structure (a parsed knowledge graph of the library) to flag methods and parameters that don't actually exist, then feeding the report back so the agent can fix them.

## Contrasts with

- [Context7](./context7.md) - A hosted MCP RAG server for 8,000+ library docs; impressive but too broad, can't include private repos, and its core logic is closed-source.

## Works with

- [Crawl4AI](./crawl4ai.md) - The open-source crawling library powering Cole's RAG MCP server, handling llms.txt, sitemaps, and recursive scraping.
- [Neo4j](./neo4j.md) - The graph database used to store and visualize the repository knowledge graph.
- [Supabase](./supabase.md) - A single platform providing Postgres for scalable chat memory plus the pgvector store for RAG embeddings, recommended over in-memory alternatives.

## Related

- [Archon](./archon.md) - Cole's free, open-source AI command center and harness builder for AI coding; started as an AI agent that builds other AI agents.

## Sources

- [All the BEST RAG Strategies in ONE MCP for AI Coding Assistants](../../sources/all-the-best-rag-strategies-in-one-mcp-for-ai-coding-assistants.md) - "[0:00:17] it is my Crawl4AI rag MCP server. And recently, I've added in a few new rag strategies into the mix."
- [AI Code That Fixes Itself (An MCP You Can Try Now)](../../sources/ai-code-that-fixes-itself-an-mcp-you-can-try-now.md) - "[0:13:12] if you want to use the knowledger graph part of this MCP server and your environment variables, you just have to set use knowledge graph to true."
