io.github.scrapeless-ai/scrapeless-mcp-server MCP Server

Scrapeless MCP Server

Welcome to the official Scrapeless Model Context Protocol (MCP) Server — a powerful integration layer that empowers LLMs, AI Agents, and AI applications to interact with the web in real time.

Built on the open MCP standard, Scrapeless MCP Server seamlessly connects models like ChatGPT, Claude, and tools like Cursor and Windsurf to a wide range of external capabilities, including:

• Google services integration (Search, Trends)
• Browser automation for page-level navigation and interaction
• Scrape dynamic, JS-heavy sites—export as HTML, Markdown, or screenshots
• Crawl entire websites by following links and capture each page in multiple formats
• AI Scraper Create an AI Scraper task for ChatGPT, Gemini, Perplexity, Copilot, Google AI Mode, Google AI Overview, Grok, or Alexa

Whether you're building an AI research assistant, a coding copilot, or autonomous web agents, this server provides the dynamic context and real-world data your workflows need—without getting blocked.

Usage Examples

1. Automated Web Interaction and Data Extraction with Claude

Using Scrapeless MCP Browser, Claude can perform complex tasks such as web navigation, clicking, scrolling, and scraping through conversational commands, with real-time preview of web interaction results via live sessions.

2. Bypassing Cloudflare to Retrieve Target Page Content

Using the Scrapeless MCP Browser service, the Cloudflare page is automatically accessed, and after the process is completed, the page content is extracted and returned in Markdown format.

3. Extracting Dynamically Rendered Page Content and Writing to File

Using the Scrapeless MCP Universal API, the JavaScript-rendered content of the target page above is scraped, exported in Markdown format, and finally written to a local file named text.md.

4. Automated SERP Scraping

Using the Scrapeless MCP Server, query the keyword “web scraping” on Google Search, retrieve the first 10 search results (including title, link, and summary), and write the content to the file named serp.text.

Here are some additional examples of how to use these servers:

Setup Guide

1. Get Scrapeless Key

• Log in to the Scrapeless Dashboard（Free trial available）
• Then click "Setting" on the left -> select "API Key Management" -> click "Create API Key". Finally, click the API Key you created to copy it.

2. Configure Your MCP Client

Scrapeless MCP Server supports both Stdio and Streamable HTTP transport modes.

🖥️ Stdio (Local Execution)

{
  "mcpServers": {
    "Scrapeless MCP Server": {
      "command": "npx",
      "args": ["-y", "scrapeless-mcp-server"],
      "env": {
        "SCRAPELESS_API_KEY": "YOUR_SCRAPELESS_KEY"
      }
    }
  }
}

🌐 Streamable HTTP (Hosted API Mode)

{
  "mcpServers": {
    "Scrapeless MCP Server": {
      "type": "streamable-http",
      "url": "https://api.scrapeless.com/mcp",
      "headers": {
        "x-api-token": "YOUR_SCRAPELESS_KEY"
      },
      "disabled": false,
      "alwaysAllow": []
    }
  }
}

Advanced Options

Customize browser session behavior with optional parameters. These can be set via environment variables (for Stdio) or HTTP headers (for Streamable HTTP):

Integration with Claude Desktop

1. Open Claude Desktop
2. Navigate to: Settings → Tools → MCP Servers
3. Click "Add MCP Server"
4. Paste either the Stdio or Streamable HTTP config above
5. Save and enable the server
6. Claude will now be able to issue web queries, extract content, and interact with pages using Scrapeless

Integration with Cursor IDE

1. Open Cursor
2. Press Cmd + Shift + P and search for: Configure MCP Servers
3. Add the Scrapeless MCP config using the format above
4. Save the file and restart Cursor (if needed)
5. Now you can ask Cursor things like:
   1. "Search StackOverflow for a solution to this error"
   2. "Scrape the HTML from this page"
6. And it will use Scrapeless in the background.

Supported MCP Tools

Security Best Practices

When using Scrapeless MCP Server with LLMs (like ChatGPT, Claude, or Cursor), it's critical to handle all scraped or extracted web content with care. Web data is untrusted by default, and improper handling may expose your application to prompt injection or other security vulnerabilities.

✅ Recommended Practices

• Never pass raw scraped content directly into LLM prompts. Raw HTML, JavaScript, or user-generated text may contain hidden injection payloads.
• Sanitize and validate all extracted content. Strip or escape potentially harmful tags and scripts before using content in downstream logic or AI models.
• Prefer structured extraction to free-form text. Use tools like scrape_html, scrape_markdown, or targeted browser_get_text with known-safe selectors to extract only the content you trust.
• Apply domain or selector whitelisting when scraping dynamically generated pages, to restrict data flow to known and trusted sources.
• Log and monitor all outbound requests made via browser or scraping tools, especially if you're handling sensitive data, tokens, or internal network access.

🚫 Avoid

• Injecting scraped HTML directly into prompts
• Letting users specify arbitrary URLs or CSS selectors without validation
• Storing unfiltered scraped content for future prompt usage

Community

• MCP Server Discord

Contact Us

For questions, suggestions, or collaboration inquiries, feel free to contact us via:

• Email: market@scrapeless.com
• Official Website: https://www.scrapeless.com
• Community Forum: https://discord.gg/Np4CAHxB9a