Web scraping just got simpler. Instead of wrestling with HTML selectors and brittle scripts, you can now describe what data you need and let AI handle the extraction. This guide shows you how to build an intelligent scraper using Crawlbase Web MCP—no coding required.

🛠️ What You’ll Need

Before starting, gather these tools:

• Cursor IDE — Download from the official Cursor website
• Crawlbase account — Sign up and grab your API credentials
• Crawlbase Web MCP — Follow the setup guide to configure it

⚙️ How It Works

The system relies on three components working together:

Crawlbase’s Crawling API handles the heavy lifting—loading pages, managing proxies, and bypassing CAPTCHAs. Crawlbase Web MCP acts as a bridge, letting AI communicate with Crawlbase securely. Cursor’s AI agents interpret your instructions, extract the data, and format it cleanly.

You describe what you want. The system does the rest.

🚀 Building Your Scraper

Step 1: Open Cursor

Launch Cursor IDE. This is where you’ll give instructions to the AI agent.

Step 2: Write Your Prompt

Tell the agent exactly what you need. For example, to scrape eBay’s Best Selling Products:

Crawl eBay's Best Selling Products page at https://www.ebay.com/str/bestsellingproducts as raw HTML. Extract the title, price, condition, seller info, and product URL for each product. Output the result in a valid JSON-formatted file named output.json.

Hit Approve when prompted.

Step 3: Let the AI Work

The agent takes over:

1. Loads the page through Crawlbase Web MCP
2. Parses the HTML
3. Extracts the specified fields
4. Generates the JSON file

No manual parsing. No selector maintenance.

Step 4: Review Results

Check the generated JSON file. For eBay, the agent typically extracts around ten products with all requested details, cleaned and ready to use.

✨ Best Practices

Be Specific

Vague prompts produce vague results. Instead of "Get data from this website," try: "Extract the product name, price, rating, and seller from each product card."

Define the Format

Specify your output structure upfront:

Output as JSON with keys: title (string), price (number), condition (string), url (string)

Handle Missing Data

Real pages are messy. Tell the agent what to do when fields don’t exist:

If a field is missing, set it to null. If a product is out of stock, still include it but add availability: false.

💡 Why AI Scraping Works Better

✅ Zero Code — Describe the data you need. Skip the CSS selectors and XPath expressions.

✅ Adapts to Changes — When sites update their layouts, the agent adjusts. It interprets content rather than relying on fragile selectors.

✅ Smart Extraction — Prices get recognized regardless of format. Seller details get captured even when positioned differently across listings.

✅ Flexible Output — Want CSV instead of JSON? Just ask. Same prompt, different format.

✅ Complete Infrastructure — Crawlbase handles JavaScript rendering, proxy rotation, CAPTCHA bypassing, and session management in the background.

📊 Common Use Cases

• Market Research — Monitor competitor pricing and inventory across multiple stores
• Price Tracking — Automate regular price checks and get alerts when changes occur
• Product Discovery — Identify trending items and bestsellers quickly
• Data Collection — Build clean datasets without manual copy-paste
• Content Aggregation — Compile catalogs from multiple sources efficiently

💰 Cost Comparison

Approach	Annual Cost
Traditional scraping	$8,000 - $25,000
AI-powered scraping	$600 - $4,000

Most teams save 70% to 90% while getting faster setup and zero maintenance.

🎯 Getting Started

The eBay example is a good starting point. Set up your Crawlbase account, enable Web MCP, open Cursor, and run the prompt. It takes a few minutes to see how much time this approach saves.

Once you’ve tested it, you can apply the same process to any site you need to scrape. The workflow stays consistent—only the prompt changes.

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🤔 FAQ

What’s the best web scraper for AI automation? Crawlbase Web MCP combined with Claude or GPT-4 provides the most complete automation. It handles dynamic content while the LLM interprets and extracts data. How do I make an AI web scraper?

1. Choose your tools (Crawlbase Web MCP + Claude/GPT API)
2. Load the page via Crawlbase
3. Extract HTML content
4. Send to LLM with extraction instructions
5. Parse the structured response
6. Loop through pages as needed Can AI actually handle web scraping? Yes. AI enhances scraping by understanding unstructured layouts, adapting to site changes, extracting semantic meaning, and handling variations automatically. It doesn’t replace traditional scrapers but makes them more resilient and intelligent.

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Found this helpful? Drop a ❤️ and let me know what you’d like to scrape next in the comments!