Crawl4AI Web Scraper MCP Server

This project provides an MCP (Model Context Protocol) server that uses the crawl4ai library to perform web scraping and intelligent content extraction tasks. It allows AI agents (like Claude, or agents built with LangChain/LangGraph) to interact with web pages, retrieve content, search for specific text, and perform LLM-based extraction based on natural language instructions.

This server uses:

• FastMCP: For creating the MCP server endpoint.
• crawl4ai: For the core web crawling and extraction logic.
• dotenv: For managing API keys via a .env file.
• (Optional) Docker: For containerized deployment, bundling Python and dependencies.

Features

• Exposes MCP tools for web interaction:
  • scrape_url: Get the full content of a webpage in Markdown format.
  • extract_text_by_query: Find specific text snippets on a page based on a query.
  • smart_extract: Use an LLM (currently Google Gemini) to extract structured information based on instructions.
• Configurable via environment variables (API keys).
• Includes Docker configuration (Dockerfile) for easy, self-contained deployment.
• Communicates over Server-Sent Events (SSE) on port 8002 by default.

Exposed MCP Tools

scrape_url

Scrape a webpage and return its content in Markdown format.

Arguments:

• url (str, required): The URL of the webpage to scrape.

Returns:

• (str): The webpage content in Markdown format, or an error message.

extract_text_by_query

Extract relevant text snippets from a webpage that contain a specific search query. Returns up to the first 5 matches found.

Arguments:

• url (str, required): The URL of the webpage to search within.
• query (str, required): The text query to search for (case-insensitive).
• context_size (int, optional): The number of characters to include before and after the matched query text in each snippet. Defaults to 300.

Returns:

• (str): A formatted string containing the found text snippets or a message indicating no matches were found, or an error message.

smart_extract

Intelligently extract specific information from a webpage using the configured LLM (currently requires Google Gemini API key) based on a natural language instruction.

Arguments:

• url (str, required): The URL of the webpage to analyze and extract from.
• instruction (str, required): Natural language instruction specifying what information to extract (e.g., "List all the speakers mentioned on this page", "Extract the main contact email address", "Summarize the key findings").

Returns:

• (str): The extracted information (often formatted as JSON or structured text based on the instruction) or a message indicating no relevant information was found, or an error message (including if the required API key is missing).

Setup and Running

You can run this server either locally or using the provided Docker configuration.

Option 1: Running with Docker (Recommended for Deployment)

This method bundles Python and all necessary libraries. You only need Docker installed on the host machine.

1. Install Docker: Download and install Docker Desktop for your OS. Start Docker Desktop.
2. Clone Repository:

   git clone https://github.com/your-username/your-repo-name.git # Replace with your repo URL
   cd your-repo-name
   

3. Create .env File: Create a file named .env in the project root directory and add your API keys:

   # Required fo
   

... View full README on GitHub