WebScraping.AI MCP Server

WebScraping.AI MCP Server

By webscraping-ai GitHub

A Model Context Protocol (MCP) server implementation that integrates with WebScraping.AI for web data extraction capabilities.

Overview

What is WebScraping.AI MCP Server?

WebScraping.AI MCP Server is a Model Context Protocol (MCP) server that integrates with WebScraping.AI to provide web data extraction capabilities.

How to use WebScraping.AI MCP Server?

To use the server, you can run it using npx with your WebScraping.AI API key or manually install it by cloning the repository and running the server.

Key features of WebScraping.AI MCP Server?

  • Question answering about web page content
  • Structured data extraction from web pages
  • HTML content retrieval with JavaScript rendering
  • Plain text extraction from web pages
  • CSS selector-based content extraction
  • Multiple proxy types with country selection
  • Concurrent request management with rate limiting
  • Custom JavaScript execution on target pages
  • Device emulation (desktop, mobile, tablet)
  • Account usage monitoring

Use cases of WebScraping.AI MCP Server?

  1. Extracting product information from e-commerce websites.
  2. Gathering data for market research from various online sources.
  3. Automating data collection for academic research.

FAQ from WebScraping.AI MCP Server?

  • Is the server free to use?

The server is free to use, but you need a valid WebScraping.AI API key.

  • Can I run multiple requests concurrently?

Yes, you can configure the maximum number of concurrent requests.

  • What types of proxies are supported?

The server supports both datacenter and residential proxies.

Content

WebScraping.AI MCP Server

A Model Context Protocol (MCP) server implementation that integrates with WebScraping.AI for web data extraction capabilities.

Features

  • Question answering about web page content
  • Structured data extraction from web pages
  • HTML content retrieval with JavaScript rendering
  • Plain text extraction from web pages
  • CSS selector-based content extraction
  • Multiple proxy types (datacenter, residential) with country selection
  • JavaScript rendering using headless Chrome/Chromium
  • Concurrent request management with rate limiting
  • Custom JavaScript execution on target pages
  • Device emulation (desktop, mobile, tablet)
  • Account usage monitoring

Installation

Running with npx

env WEBSCRAPING_AI_API_KEY=your_api_key npx -y webscraping-ai-mcp

Manual Installation

# Clone the repository
git clone https://github.com/webscraping-ai/webscraping-ai-mcp-server.git
cd webscraping-ai-mcp-server

# Install dependencies
npm install

# Run
npm start

Running on Cursor

Configuring Cursor 🖥️ Note: Requires Cursor version 0.45.6+

To configure WebScraping.AI MCP in Cursor:

  1. Open Cursor Settings
  2. Go to Features > MCP Servers
  3. Click "+ Add New MCP Server"
  4. Enter the following:
    • Name: "webscraping-ai-mcp" (or your preferred name)
    • Type: "command"
    • Command: env WEBSCRAPING_AI_API_KEY=your-api-key npx -y webscraping-ai-mcp

If you are using Windows and are running into issues, try cmd /c "set WEBSCRAPING_AI_API_KEY=your-api-key && npx -y webscraping-ai-mcp"

Replace your-api-key with your WebScraping.AI API key.

Running on Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "mcp-server-webscraping-ai": {
      "command": "npx",
      "args": ["-y", "webscraping-ai-mcp"],
      "env": {
        "WEBSCRAPING_AI_API_KEY": "YOUR_API_KEY_HERE",
        "WEBSCRAPING_AI_CONCURRENCY_LIMIT": "5"
      }
    }
  }
}

Configuration

Environment Variables

Required

  • WEBSCRAPING_AI_API_KEY: Your WebScraping.AI API key
Concurrency Configuration
  • WEBSCRAPING_AI_CONCURRENCY_LIMIT: Maximum number of concurrent requests (default: 5)

Configuration Examples

For standard usage with custom concurrency setting:

# Required
export WEBSCRAPING_AI_API_KEY=your-api-key
# Optional
export WEBSCRAPING_AI_CONCURRENCY_LIMIT=10        # Increase concurrency limit

Available Tools

1. Question Tool (webscraping_ai_question)

Ask questions about web page content.

{
  "name": "webscraping_ai_question",
  "arguments": {
    "url": "https://example.com",
    "question": "What is the main topic of this page?",
    "timeout": 30000,
    "js": true,
    "js_timeout": 2000,
    "wait_for": ".content-loaded",
    "proxy": "datacenter",
    "country": "us"
  }
}

Example response:

{
  "content": [
    {
      "type": "text",
      "text": "The main topic of this page is examples and documentation for HTML and web standards."
    }
  ],
  "isError": false
}

2. Fields Tool (webscraping_ai_fields)

Extract structured data from web pages based on instructions.

{
  "name": "webscraping_ai_fields",
  "arguments": {
    "url": "https://example.com/product",
    "fields": {
      "title": "Extract the product title",
      "price": "Extract the product price",
      "description": "Extract the product description"
    },
    "js": true,
    "timeout": 30000
  }
}

Example response:

{
  "content": [
    {
      "type": "text",
      "text": {
        "title": "Example Product",
        "price": "$99.99",
        "description": "This is an example product description."
      }
    }
  ],
  "isError": false
}

3. HTML Tool (webscraping_ai_html)

Get the full HTML of a web page with JavaScript rendering.

{
  "name": "webscraping_ai_html",
  "arguments": {
    "url": "https://example.com",
    "js": true,
    "timeout": 30000,
    "wait_for": "#content-loaded"
  }
}

Example response:

{
  "content": [
    {
      "type": "text",
      "text": "<html>...[full HTML content]...</html>"
    }
  ],
  "isError": false
}

4. Text Tool (webscraping_ai_text)

Extract the visible text content from a web page.

{
  "name": "webscraping_ai_text",
  "arguments": {
    "url": "https://example.com",
    "js": true,
    "timeout": 30000
  }
}

Example response:

{
  "content": [
    {
      "type": "text",
      "text": "Example Domain\nThis domain is for use in illustrative examples in documents..."
    }
  ],
  "isError": false
}

5. Selected Tool (webscraping_ai_selected)

Extract content from a specific element using a CSS selector.

{
  "name": "webscraping_ai_selected",
  "arguments": {
    "url": "https://example.com",
    "selector": "div.main-content",
    "js": true,
    "timeout": 30000
  }
}

Example response:

{
  "content": [
    {
      "type": "text",
      "text": "<div class=\"main-content\">This is the main content of the page.</div>"
    }
  ],
  "isError": false
}

6. Selected Multiple Tool (webscraping_ai_selected_multiple)

Extract content from multiple elements using CSS selectors.

{
  "name": "webscraping_ai_selected_multiple",
  "arguments": {
    "url": "https://example.com",
    "selectors": ["div.header", "div.product-list", "div.footer"],
    "js": true,
    "timeout": 30000
  }
}

Example response:

{
  "content": [
    {
      "type": "text",
      "text": [
        "<div class=\"header\">Header content</div>",
        "<div class=\"product-list\">Product list content</div>",
        "<div class=\"footer\">Footer content</div>"
      ]
    }
  ],
  "isError": false
}

7. Account Tool (webscraping_ai_account)

Get information about your WebScraping.AI account.

{
  "name": "webscraping_ai_account",
  "arguments": {}
}

Example response:

{
  "content": [
    {
      "type": "text",
      "text": {
        "requests": 5000,
        "remaining": 4500,
        "limit": 10000,
        "resets_at": "2023-12-31T23:59:59Z"
      }
    }
  ],
  "isError": false
}

Common Options for All Tools

The following options can be used with all scraping tools:

  • timeout: Maximum web page retrieval time in ms (15000 by default, maximum is 30000)
  • js: Execute on-page JavaScript using a headless browser (true by default)
  • js_timeout: Maximum JavaScript rendering time in ms (2000 by default)
  • wait_for: CSS selector to wait for before returning the page content
  • proxy: Type of proxy, datacenter or residential (residential by default)
  • country: Country of the proxy to use (US by default). Supported countries: us, gb, de, it, fr, ca, es, ru, jp, kr, in
  • custom_proxy: Your own proxy URL in "http://user:password@host:port" format
  • device: Type of device emulation. Supported values: desktop, mobile, tablet
  • error_on_404: Return error on 404 HTTP status on the target page (false by default)
  • error_on_redirect: Return error on redirect on the target page (false by default)
  • js_script: Custom JavaScript code to execute on the target page

Error Handling

The server provides robust error handling:

  • Automatic retries for transient errors
  • Rate limit handling with backoff
  • Detailed error messages
  • Network resilience

Example error response:

{
  "content": [
    {
      "type": "text",
      "text": "API Error: 429 Too Many Requests"
    }
  ],
  "isError": true
}

Integration with LLMs

This server implements the Model Context Protocol, making it compatible with any MCP-enabled LLM platforms. You can configure your LLM to use these tools for web scraping tasks.

Example: Configuring Claude with MCP

// Example code for connecting Claude with the WebScraping.AI MCP Server
const { Claude } = require('@anthropic-ai/sdk');
const { McpClient } = require('@modelcontextprotocol/sdk/client');

const claude = new Claude({
  apiKey: 'your_claude_api_key'
});

const mcpClient = new McpClient({
  baseUrl: 'http://localhost:3000/sse'
});

// Now you can use Claude with WebScraping.AI tools
const response = await claude.messages.create({
  model: 'claude-3-opus-20240229',
  max_tokens: 1000,
  system: 'You have access to WebScraping.AI tools for web data extraction.',
  messages: [
    { role: 'user', content: 'Extract the main heading from https://example.com' }
  ],
  tools: await mcpClient.listTools()
});

Development

# Clone the repository
git clone https://github.com/webscraping-ai/webscraping-ai-mcp-server.git
cd webscraping-ai-mcp-server

# Install dependencies
npm install

# Run tests
npm test

# Add your .env file
cp .env.example .env

# Start the inspector
npx @modelcontextprotocol/inspector node src/index.js

Contributing

  1. Fork the repository
  2. Create your feature branch
  3. Run tests: npm test
  4. Submit a pull request

License

MIT License - see LICENSE file for details

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