🐳 Crawl4AI+SearXNG MCP Server

Web Crawling, Search and RAG Capabilities for AI Agents and AI Coding Assistants

(FORKED FROM https://github.com/coleam00/mcp-crawl4ai-rag). Added SearXNG integration and batch scrape and processing capabilities.

A self-contained Docker solution that combines the Model Context Protocol (MCP), Crawl4AI, SearXNG, and Supabase to provide AI agents and coding assistants with complete web search, crawling, and RAG capabilities.

🚀 Complete Stack in One Command: Deploy everything with docker compose up -d - no Python setup, no dependencies, no external services required.

🎯 Smart RAG vs Traditional Scraping

Unlike traditional scraping (such as Firecrawl) that dumps raw content and overwhelms LLM context windows, this solution uses intelligent RAG (Retrieval Augmented Generation) to:

🔍 Extract only relevant content using semantic similarity search
⚡ Prevent context overflow by returning focused, pertinent information
🧠 Enhance AI responses with precisely targeted knowledge
📊 Maintain context efficiency for better LLM performance

Flexible Output Options:

RAG Mode (default): Returns semantically relevant chunks with similarity scores
Raw Markdown Mode: Full content extraction when complete context is needed
Hybrid Search: Combines semantic and keyword search for comprehensive results

💡 Key Benefits

🔧 Zero Configuration: Pre-configured SearXNG instance included
🐳 Docker-Only: No Python environment setup required
🔍 Integrated Search: Built-in SearXNG for private, fast search
⚡ Production Ready: HTTPS, security, and monitoring included
🎯 AI-Optimized: RAG strategies built for coding assistants

Overview

This Docker-based MCP server provides a complete web intelligence stack that enables AI agents to:

Search the web using the integrated SearXNG instance
Crawl and scrape websites with advanced content extraction
Store content in vector databases with intelligent chunking
Perform RAG queries with multiple enhancement strategies

Advanced RAG Strategies Available:

Contextual Embeddings for enriched semantic understanding
Hybrid Search combining vector and keyword search
Agentic RAG for specialized code example extraction
Reranking for improved result relevance using cross-encoder models
Knowledge Graph for AI hallucination detection and repository code analysis

See the Configuration section below for details on how to enable and configure these strategies.

Features

Smart URL Detection: Automatically detects and handles different URL types (regular webpages, sitemaps, text files)
Recursive Crawling: Follows internal links to discover content
Parallel Processing: Efficiently crawls multiple pages simultaneously
Content Chunking: Intelligently splits content by headers and size for better processing
Vector Search: Performs RAG over crawled content, optionally filtering by data source for precision
Source Retrieval: Retrieve sources available for filtering to guide the RAG process

Tools

The server provides essential web crawling and search tools:

Core Tools (Always Available)

1. **scrape_urls**: Scrape one or more URLs and store their content in the vector database. Supports both single URLs and lists of URLs for batch processing.

2. **smart_crawl_url**: Intelligently crawl a full website based on the type of URL provided (sitemap, llms-full.txt, or a regular webpage that needs to be crawled recursively)

3. **get_available_sources**: Get a list of all available sources (domains) in the database

4. **perform_rag_query**: Search for relevant content using semantic search with optional source filtering

5. NEW! **search**: Comprehensive web search tool that integrates SearXNG search with automated scraping and RAG processing. Performs a complete workflow: (1) searches SearXNG with the provided query, (2) extracts URLs from search results, (3) automatically scrapes all found URLs using existing scraping infrastructure, (4) stores content in vector database, and (5) returns either RAG-processed results organized by URL or raw markdown content. Key parameters: query (search terms), return_raw_markdown (bypasses RAG for raw content), num_results (search result limit), batch_size (database operation batching), max_concurrent (parallel scraping sessions). Ideal for research workflows, competitive analysis, and content discovery with built-in intelligence.

Conditional Tools

6. **search_code_examples** (requires USE_AGENTIC_RAG=true): Search specifically for code examples and their summaries from crawled documentation. This tool provides targeted code snippet retrieval for AI coding assistants.

Knowledge Graph Tools (requires `USE_KNOWLEDGE_GRAPH=true`, see below)

7. **parse_github_repository**: Parse a GitHub repository into a Neo4j knowledge graph, extracting classes, methods, functions, and their relationships for hallucination detection

8. **check_ai_script_hallucinations**: Analyze Python scripts for AI hallucinations by validating imports, method calls, and class usage against the knowledge graph

9. **query_knowledge_graph**: Explore and query the Neo4j knowledge graph with commands like repos, classes, methods, and custom Cypher queries

Prerequisites

Required:

Docker and Docker Compose - This is a Docker-only solution
Supabase account - For vector database and RAG functionality
OpenAI API key - For generating embeddings

Optional:

Neo4j instance - For knowledge graph functionality (see Knowledge Graph Setup)
Custom domain - For production HTTPS deployment

Installation

This is a Docker-only solution - no Python environment setup required!

Quick Start

1. Clone this repository:

bash

git clone https://github.com/coleam00/mcp-crawl4ai-rag.git
   cd mcp-crawl4ai-rag

2. Configure environment:

bash

cp .env.example .env
   # Edit .env with your API keys (see Configuration section below)

3. Deploy the complete stack:

bash

docker compose up -d

That's it! Your complete search, crawl, and RAG stack is now running:

MCP Server: http://localhost:8051
SearXNG Search: http://localhost:8080 (internal)
Caddy Proxy: Handles HTTPS and routing

What Gets Deployed

The Docker Compose stack includes:

MCP Crawl4AI Server - Main application server
SearXNG - Private search engine instance
Valkey - Redis-compatible cache for SearXNG
Caddy - Reverse proxy with automatic HTTPS

Database Setup IMPORTANT!

Before running the server, you need to set up the database with the pgvector extension:

1. Go to the SQL Editor in your Supabase dashboard (create a new project first if necessary)

2. Create a new query and paste the contents of crawled_pages.sql

3. Run the query to create the necessary tables and functions

Knowledge Graph Setup (Optional)

To enable AI hallucination detection and repository analysis features, you need to set up Neo4j.

Note: The knowledge graph functionality works fully with Docker and supports all features.

Neo4j Setup Options

Option 1: Local AI Package (Recommended)

The easiest way to get Neo4j running is with the Local AI Package:

1. Clone and start Neo4j:

bash

git clone https://github.com/coleam00/local-ai-packaged.git
   cd local-ai-packaged
   # Follow repository instructions to start Neo4j with Docker Compose

2. Connection details for Docker:

URI: bolt://host.docker.internal:7687 (for Docker containers)
URI: bolt://localhost:7687 (for local access)
Username: neo4j
Password: Check Local AI Package documentation

Option 2: Neo4j Docker

Run Neo4j directly with Docker:

bash

docker run -d \
  --name neo4j \
  -p 7474:7474 -p 7687:7687 \
  -e NEO4J_AUTH=neo4j/your-password \
  neo4j:latest

Option 3: Neo4j Desktop

Use Neo4j Desktop for a local GUI-based installation:

1. Download and install: Neo4j Desktop

2. Create a new database with your preferred settings

3. Connection details:

URI: bolt://host.docker.internal:7687 (for Docker containers)
URI: bolt://localhost:7687 (for local access)
Username: neo4j
Password: Whatever you set during database creation

Configuration

Configure the Docker stack by editing your .env file (copy from .env.example):

bash

# ========================================
# MCP SERVER CONFIGURATION
# ========================================
TRANSPORT=sse
HOST=0.0.0.0
PORT=8051

# ========================================
# INTEGRATED SEARXNG CONFIGURATION
# ========================================
# Pre-configured for Docker Compose - SearXNG runs internally
SEARXNG_URL=http://searxng:8080
SEARXNG_USER_AGENT=MCP-Crawl4AI-RAG-Server/1.0
SEARXNG_DEFAULT_ENGINES=google,bing,duckduckgo
SEARXNG_TIMEOUT=30

# Optional: Custom domain for production HTTPS
SEARXNG_HOSTNAME=http://localhost
# SEARXNG_TLS=your-email@example.com  # For Let's Encrypt

# ========================================
# AI SERVICES CONFIGURATION
# ========================================
# Required: OpenAI API for embeddings
OPENAI_API_KEY=your_openai_api_key

# LLM for summaries and contextual embeddings
MODEL_CHOICE=gpt-4.1-nano-2025-04-14

# Required: Supabase for vector database
SUPABASE_URL=your_supabase_project_url
SUPABASE_SERVICE_KEY=your_supabase_service_key

# ========================================
# RAG ENHANCEMENT STRATEGIES
# ========================================
USE_CONTEXTUAL_EMBEDDINGS=false
USE_HYBRID_SEARCH=false
USE_AGENTIC_RAG=false
USE_RERANKING=false
USE_KNOWLEDGE_GRAPH=false

# Optional: Neo4j for knowledge graph (if USE_KNOWLEDGE_GRAPH=true)
# Use host.docker.internal:7687 for Docker Desktop on Windows/Mac
NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=your_neo4j_password

Key Configuration Notes

🔍 SearXNG Integration: The stack includes a pre-configured SearXNG instance that runs automatically. No external setup required!

🐳 Docker Networking: The default configuration uses Docker internal networking (http://searxng:8080) which works out of the box.

🔐 Production Setup: For production, set SEARXNG_HOSTNAME to your domain and SEARXNG_TLS to your email for automatic HTTPS.

RAG Strategy Options

The Crawl4AI RAG MCP server supports four powerful RAG strategies that can be enabled independently:

1. USE_CONTEXTUAL_EMBEDDINGS

When enabled, this strategy enhances each chunk's embedding with additional context from the entire document. The system passes both the full document and the specific chunk to an LLM (configured via MODEL_CHOICE) to generate enriched context that gets embedded alongside the chunk content.

When to use: Enable this when you need high-precision retrieval where context matters, such as technical documentation where terms might have different meanings in different sections.
Trade-offs: Slower indexing due to LLM calls for each chunk, but significantly better retrieval accuracy.
Cost: Additional LLM API calls during indexing.

2. USE_HYBRID_SEARCH

Combines traditional keyword search with semantic vector search to provide more comprehensive results. The system performs both searches in parallel and intelligently merges results, prioritizing documents that appear in both result sets.

When to use: Enable this when users might search using specific technical terms, function names, or when exact keyword matches are important alongside semantic understanding.
Trade-offs: Slightly slower search queries but more robust results, especially for technical content.
Cost: No additional API costs, just computational overhead.

3. USE_AGENTIC_RAG

Enables specialized code example extraction and storage. When crawling documentation, the system identifies code blocks (≥300 characters), extracts them with surrounding context, generates summaries, and stores them in a separate vector database table specifically designed for code search.

When to use: Essential for AI coding assistants that need to find specific code examples, implementation patterns, or usage examples from documentation.
Trade-offs: Significantly slower crawling due to code extraction and summarization, requires more storage space.
Cost: Additional LLM API calls for summarizing each code example.
Benefits: Provides a dedicated search_code_examples tool that AI agents can use to find specific code implementations.

4. USE_RERANKING

Applies cross-encoder reranking to search results after initial retrieval. Uses a lightweight cross-encoder model (cross-encoder/ms-marco-MiniLM-L-6-v2) to score each result against the original query, then reorders results by relevance.

When to use: Enable this when search precision is critical and you need the most relevant results at the top. Particularly useful for complex queries where semantic similarity alone might not capture query intent.
Trade-offs: Adds ~100-200ms to search queries depending on result count, but significantly improves result ordering.
Cost: No additional API costs - uses a local model that runs on CPU.
Benefits: Better result relevance, especially for complex queries. Works with both regular RAG search and code example search.

5. USE_KNOWLEDGE_GRAPH

Enables AI hallucination detection and repository analysis using Neo4j knowledge graphs. When enabled, the system can parse GitHub repositories into a graph database and validate AI-generated code against real repository structures. Fully compatible with Docker - all functionality works within the containerized environment.

When to use: Enable this for AI coding assistants that need to validate generated code against real implementations, or when you want to detect when AI models hallucinate non-existent methods, classes, or incorrect usage patterns.
Trade-offs: Requires Neo4j setup and additional dependencies. Repository parsing can be slow for large codebases, and validation requires repositories to be pre-indexed.
Cost: No additional API costs for validation, but requires Neo4j infrastructure (can use free local installation or cloud AuraDB).
Benefits: Provides three powerful tools: parse_github_repository for indexing codebases, check_ai_script_hallucinations for validating AI-generated code, and query_knowledge_graph for exploring indexed repositories.

Usage with MCP Tools:

You can tell the AI coding assistant to add a Python GitHub repository to the knowledge graph:

"Add https://github.com/pydantic/pydantic-ai.git to the knowledge graph"

Make sure the repo URL ends with .git.

You can also have the AI coding assistant check for hallucinations with scripts it creates using the MCP check_ai_script_hallucinations tool.

Recommended Configurations

For general documentation RAG:

code

USE_CONTEXTUAL_EMBEDDINGS=false
USE_HYBRID_SEARCH=true
USE_AGENTIC_RAG=false
USE_RERANKING=true

For AI coding assistant with code examples:

code

USE_CONTEXTUAL_EMBEDDINGS=true
USE_HYBRID_SEARCH=true
USE_AGENTIC_RAG=true
USE_RERANKING=true
USE_KNOWLEDGE_GRAPH=false

For AI coding assistant with hallucination detection:

code

USE_CONTEXTUAL_EMBEDDINGS=true
USE_HYBRID_SEARCH=true
USE_AGENTIC_RAG=true
USE_RERANKING=true
USE_KNOWLEDGE_GRAPH=true

For fast, basic RAG:

code

USE_CONTEXTUAL_EMBEDDINGS=false
USE_HYBRID_SEARCH=true
USE_AGENTIC_RAG=false
USE_RERANKING=false
USE_KNOWLEDGE_GRAPH=false

Running the Server

The complete stack is managed through Docker Compose:

Start the Stack

bash

docker compose up -d

View Logs

bash

# All services
docker compose logs -f

# Specific service
docker compose logs -f mcp-crawl4ai
docker compose logs -f searxng

Stop the Stack

bash

docker compose down

Restart Services

bash

# Restart all
docker compose restart

# Restart specific service
docker compose restart mcp-crawl4ai

The MCP server will be available at http://localhost:8051 for SSE connections.

Integration with MCP Clients

After starting the Docker stack with docker compose up -d, your MCP server will be available for integration.

SSE Configuration (Recommended)

The Docker stack runs with SSE transport by default. Connect using:

Claude Desktop/Windsurf:

json

{
  "mcpServers": {
    "crawl4ai-rag": {
      "transport": "sse",
      "url": "http://localhost:8051/sse"
    }
  }
}

Windsurf (alternative syntax):

json

{
  "mcpServers": {
    "crawl4ai-rag": {
      "transport": "sse",
      "serverUrl": "http://localhost:8051/sse"
    }
  }
}

Claude Code CLI:

bash

claude mcp add-json crawl4ai-rag '{"type":"http","url":"http://localhost:8051/sse"}' --scope user

Docker Networking Notes

Same machine: Use http://localhost:8051/sse
Different container: Use http://host.docker.internal:8051/sse
Remote access: Replace localhost with your server's IP address

Production Deployment

For production use with custom domains:

1. **Update your .env**:

bash

SEARXNG_HOSTNAME=https://yourdomain.com
   SEARXNG_TLS=your-email@example.com

2. Access via HTTPS:

code

https://yourdomain.com:8051/sse

Health Check

Verify the server is running:

bash

curl http://localhost:8051/health

Knowledge Graph Architecture

The knowledge graph system stores repository code structure in Neo4j with the following components:

Core Components (`knowledge_graphs/` folder):

**parse_repo_into_neo4j.py**: Clones and analyzes GitHub repositories, extracting Python classes, methods, functions, and imports into Neo4j nodes and relationships
**ai_script_analyzer.py**: Parses Python scripts using AST to extract imports, class instantiations, method calls, and function usage
**knowledge_graph_validator.py**: Validates AI-generated code against the knowledge graph to detect hallucinations (non-existent methods, incorrect parameters, etc.)
**hallucination_reporter.py**: Generates comprehensive reports about detected hallucinations with confidence scores and recommendations
**query_knowledge_graph.py**: Interactive CLI tool for exploring the knowledge graph (functionality now integrated into MCP tools)

Knowledge Graph Schema:

The Neo4j database stores code structure as:

Nodes:

Repository: GitHub repositories
File: Python files within repositories
Class: Python classes with methods and attributes
Method: Class methods with parameter information
Function: Standalone functions
Attribute: Class attributes

Relationships:

Repository -[:CONTAINS]-> File
File -[:DEFINES]-> Class
File -[:DEFINES]-> Function
Class -[:HAS_METHOD]-> Method
Class -[:HAS_ATTRIBUTE]-> Attribute

Workflow:

1. Repository Parsing: Use parse_github_repository tool to clone and analyze open-source repositories

2. Code Validation: Use check_ai_script_hallucinations tool to validate AI-generated Python scripts

3. Knowledge Exploration: Use query_knowledge_graph tool to explore available repositories, classes, and methods

Troubleshooting

Docker Issues

Container won't start:

bash

# Check logs for specific errors
docker compose logs mcp-crawl4ai

# Verify configuration is valid
docker compose config

# Restart problematic services
docker compose restart mcp-crawl4ai

SearXNG not accessible:

bash

# Check if SearXNG is running
docker compose logs searxng

# Verify internal networking
docker compose exec mcp-crawl4ai curl http://searxng:8080

Port conflicts:

bash

# Check what's using ports
netstat -tulpn | grep -E ":(8051|8080)"

# Change ports in docker-compose.yml if needed
ports:
  - "8052:8051"  # Changed from 8051:8051

Common Configuration Issues

Environment variables not loading:

Ensure .env file is in the same directory as docker-compose.yml
Verify no spaces around = in .env file
Check for special characters that need quoting

API connection failures:

Verify OPENAI_API_KEY is valid and has credits
Check SUPABASE_URL and SUPABASE_SERVICE_KEY are correct
Test API connectivity from within container:

bash

docker compose exec mcp-crawl4ai curl -H "Authorization: Bearer $OPENAI_API_KEY" https://api.openai.com/v1/models

Neo4j connection issues:

Use host.docker.internal:7687 instead of localhost:7687 for Neo4j running on host
Verify Neo4j is running and accessible
Check firewall settings for port 7687

Performance Optimization

Memory usage:

bash

# Monitor resource usage
docker stats

# Adjust memory limits in docker-compose.yml
deploy:
  resources:
    limits:
      memory: 2G

Disk space:

bash

# Clean up Docker
docker system prune -a

# Check volume usage
docker volume ls

Getting Help

1. Check logs first: docker compose logs -f

2. Verify configuration: docker compose config

3. Test connectivity: Use curl commands shown above

4. Reset everything: docker compose down -v && docker compose up -d

Development & Customization

This Docker stack provides a foundation for building more complex MCP servers:

1. Modify the MCP server: Edit files in src/ and rebuild: docker compose build mcp-crawl4ai

2. Add custom tools: Extend src/crawl4ai_mcp.py with @mcp.tool() decorators

3. Customize SearXNG: Edit searxng/settings.yml and restart

4. Add services: Extend docker-compose.yml with additional containers

Crawl4ai Rag Mcp

Documentation

🎯 Smart RAG vs Traditional Scraping

💡 Key Benefits

Overview

Features

Tools

Core Tools (Always Available)

Conditional Tools

Knowledge Graph Tools (requires USE_KNOWLEDGE_GRAPH=true, see below)

Prerequisites

Installation

Quick Start

What Gets Deployed

Database Setup *IMPORTANT!*

Knowledge Graph Setup (Optional)

Neo4j Setup Options

Configuration

Key Configuration Notes

RAG Strategy Options

1. USE_CONTEXTUAL_EMBEDDINGS

2. USE_HYBRID_SEARCH

3. USE_AGENTIC_RAG

4. USE_RERANKING

5. USE_KNOWLEDGE_GRAPH

Recommended Configurations

Running the Server

Start the Stack

View Logs

Stop the Stack

Restart Services

Integration with MCP Clients

SSE Configuration (Recommended)

Docker Networking Notes

Production Deployment

Health Check

Knowledge Graph Architecture

Core Components (knowledge_graphs/ folder):

Knowledge Graph Schema:

Workflow:

Troubleshooting

Docker Issues

Common Configuration Issues

Performance Optimization

Getting Help

Development & Customization

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Knowledge Graph Tools (requires `USE_KNOWLEDGE_GRAPH=true`, see below)

Database Setup IMPORTANT!

Core Components (`knowledge_graphs/` folder):