Iflow Mcp Laurentvv Crawl4ai

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A powerful web crawling tool that integrates with AI assistants via the MCP (Model Context Protocol). This project allows AI assistants to crawl websites, extract dynamic content, navigate through links, and save structured Markdown files directly.

📋 Features

• Native integration with AI assistants via MCP
• Return scraped Markdown content directly to the AI
• Extracts and surfaces internal/external links for AI navigation
• Website crawling with configurable depth
• Detailed crawl result statistics
• Error and not found page handling
• Advanced Scraping Capabilities:
  • Magic Mode: Bypass anti-bots (like Cloudflare) and simulate real browser behavior
  • Targeted Extraction: Fetch only what you need using CSS selectors
  • Custom JavaScript: Execute code before extraction (clicks, scrolls, form fills)
  • Persistent Sessions: Keep cookies and state across requests for authenticated sites
  • SPA Support: Wait for dynamic CSS selectors or set explicit pre-extraction delays

🚀 MCP Configuration

The simplest and recommended way to use this tool is via uvx, which automatically fetches and runs the latest published version from PyPI.

Prerequisites

• uv installed on your system.

Setup for AI Assistants (e.g., Claude Desktop, Cline)

Add the following to your AI Assistant's MCP configuration file (e.g., cline_mcp_settings.json or claude_desktop_config.json):

Note for Windows Users: It is highly recommended to specify --python 3.13 to avoid compilation issues with certain dependencies.

From PyPI (recommended):

{
  "mcpServers": {
    "crawl": {
      "command": "uvx",
      "args": [
        "--python",
        "3.13",
        "crawl4ai-mcp-llm"
      ],
      "disabled": false,
      "autoApprove": [],
      "timeout": 600
    }
  }
}

From GitHub (latest unreleased):

{
  "mcpServers": {
    "crawl": {
      "command": "uvx",
      "args": [
        "--python",
        "3.13",
        "--from",
        "git+https://github.com/laurentvv/crawl4ai-mcp-llm",
        "crawl4ai-mcp-llm"
      ],
      "disabled": false,
      "autoApprove": [],
      "timeout": 600
    }
  }
}

Important: Browser Installation

The crawler uses Playwright to handle dynamic content. You must install the required browsers after setting up the tool:

uv run playwright install chromium

🖥️ Usage

Once configured, you can use the crawler by asking your AI assistant to perform a crawl.

Usage Examples with Claude/Cline

• Simple Crawl: "Can you crawl the site example.com and give me a summary?"
• Crawl with Options: "Can you crawl https://example.com with a depth of 3 and include external links?"
• Dynamic Content: "Crawl this React app and wait for the .main-content selector to load."
• Bypass Protections: "Crawl example.com but use 'magic mode' to bypass the anti-bot protection."
• Targeted Extraction: "Crawl the docs site but only extract content matching the h1, p.lead CSS selector."

🛠️ Available Parameters (MCP Tool)

The crawl tool accepts the following parameters:

Parameter	Type	Description	Default Value
url	string	URL to crawl (required)	-
max_depth	integer	Maximum crawling depth	2
include_external	boolean	Include external links	false
verbose	boolean	Enable detailed output	true
wait_for_selector	string	CSS selector to wait for before extracting content. Useful for single-page applications.	None
return_content	boolean	Whether to return the extracted content directly in the MCP response (truncated to 50k chars if necessary).	true
output_file	string	Output file path	automatically generated
magic	boolean	Enable magic mode to bypass anti-bots and simulate a real browser	false
css_selector	string	Specific CSS selector to extract only targeted elements from the page	None
js_code	string	Custom JavaScript code to execute on the page before extraction	None
session_id	string	Persistent session identifier to keep cookies and browser state across requests	None
delay_before_return_html	number	Delay in seconds to wait before extracting HTML (useful for heavy JS pages)	None

👨‍💻 Development

If you want to modify the crawler or run it locally:

1. Clone this repository:

git clone https://github.com/laurentvv/crawl4ai-mcp-llm
cd crawl4ai-mcp-llm

2. Install dependencies using uv:

uv sync

3. Test the MCP server locally using the official MCP Inspector:

npx -y @modelcontextprotocol/inspector uv run crawl4ai-mcp-llm

4. Run the automated test suite:

uv run pytest tests/

5. Run the MCP server directly (for standard usage):

uv run crawl4ai-mcp-llm

🤝 Contribution

Contributions are welcome! Feel free to open an issue or submit a pull request.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.