What is the Claude API and How Does It Work with MCP Servers?

The Claude API is Anthropic's powerful artificial intelligence interface that enables developers to integrate advanced language models into their applications. When combined with Model Context Protocol (MCP) servers, Claude becomes a sophisticated tool for web scraping, data extraction, and browser automation tasks.

Understanding the Claude API

The Claude API provides access to Anthropic's family of large language models through a RESTful HTTP interface. It supports various capabilities including:

• Text generation and analysis: Process and generate human-like text responses
• Code generation: Create, analyze, and debug code in multiple languages
• Data extraction: Extract structured data from unstructured text
• Tool use: Execute external tools and APIs through function calling
• Vision capabilities: Analyze images and screenshots (Claude 3 models)

Core API Features

The Claude API operates on a conversation-based model where you send messages and receive AI-generated responses. Each request includes:

• Model selection: Choose from different Claude versions (Opus, Sonnet, Haiku)
• System prompts: Define the AI's behavior and capabilities
• Messages: User and assistant conversation history
• Tool definitions: Declare external functions the model can call
• Parameters: Control temperature, max tokens, and other settings

Model Context Protocol (MCP) Explained

MCP is an open protocol that standardizes how AI applications communicate with external data sources and tools. Think of MCP servers as specialized microservices that provide:

• Resources: Access to external data (databases, APIs, files)
• Tools: Executable functions for performing actions
• Prompts: Pre-configured templates for common tasks
• Sampling: Request AI completions from the server side

How MCP Enhances Web Scraping

MCP servers transform Claude from a conversational AI into a powerful automation engine. For web scraping specifically, MCP provides:

1. Browser automation tools: Direct integration with Puppeteer and Playwright
2. HTTP client capabilities: Make authenticated requests to APIs
3. Data persistence: Store scraped data to databases or files
4. Workflow orchestration: Chain multiple scraping operations together

Integrating Claude API with MCP Servers

Setting Up the Environment

First, install the necessary dependencies:

Python:

pip install anthropic mcp-client

JavaScript/Node.js:

npm install @anthropic-ai/sdk @modelcontextprotocol/sdk

Configuring MCP Server Connection

Here's how to configure an MCP server for web scraping with Claude:

Python Example:

import anthropic
from mcp import Client as MCPClient

# Initialize Claude API client
claude_client = anthropic.Anthropic(
    api_key="your-api-key"
)

# Connect to MCP server
mcp_client = MCPClient("http://localhost:3000")

# List available tools from MCP server
tools = mcp_client.list_tools()
print(f"Available tools: {tools}")

# Create a message with tool use
response = claude_client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    tools=tools,
    messages=[
        {
            "role": "user",
            "content": "Scrape the product title and price from https://example.com/products"
        }
    ]
)

# Handle tool calls
if response.stop_reason == "tool_use":
    for content_block in response.content:
        if content_block.type == "tool_use":
            # Execute tool through MCP server
            tool_result = mcp_client.call_tool(
                content_block.name,
                content_block.input
            )

            # Send result back to Claude
            final_response = claude_client.messages.create(
                model="claude-3-5-sonnet-20241022",
                max_tokens=1024,
                messages=[
                    {"role": "user", "content": "Scrape the product info"},
                    {"role": "assistant", "content": response.content},
                    {
                        "role": "user",
                        "content": [
                            {
                                "type": "tool_result",
                                "tool_use_id": content_block.id,
                                "content": tool_result
                            }
                        ]
                    }
                ]
            )

JavaScript Example:

const Anthropic = require('@anthropic-ai/sdk');
const { Client: MCPClient } = require('@modelcontextprotocol/sdk');

// Initialize clients
const claude = new Anthropic({
  apiKey: process.env.ANTHROPIC_API_KEY
});

const mcpClient = new MCPClient('http://localhost:3000');

async function scrapeWithClaudeAndMCP(url) {
  // Get available tools from MCP server
  const tools = await mcpClient.listTools();

  // Send initial request to Claude
  const message = await claude.messages.create({
    model: 'claude-3-5-sonnet-20241022',
    max_tokens: 1024,
    tools: tools,
    messages: [{
      role: 'user',
      content: `Extract all product information from ${url}`
    }]
  });

  // Process tool calls
  if (message.stop_reason === 'tool_use') {
    const toolUse = message.content.find(block => block.type === 'tool_use');

    // Execute tool via MCP
    const toolResult = await mcpClient.callTool(
      toolUse.name,
      toolUse.input
    );

    // Get final response from Claude
    const finalResponse = await claude.messages.create({
      model: 'claude-3-5-sonnet-20241022',
      max_tokens: 2048,
      messages: [
        { role: 'user', content: `Extract product info from ${url}` },
        { role: 'assistant', content: message.content },
        {
          role: 'user',
          content: [{
            type: 'tool_result',
            tool_use_id: toolUse.id,
            content: JSON.stringify(toolResult)
          }]
        }
      ]
    });

    return finalResponse.content[0].text;
  }
}

// Usage
scrapeWithClaudeAndMCP('https://example.com/products')
  .then(data => console.log(data))
  .catch(err => console.error(err));

Common MCP Server Tools for Web Scraping

Browser Automation Tools

MCP servers can expose browser automation capabilities similar to handling browser sessions in Puppeteer:

{
  "name": "browser_navigate",
  "description": "Navigate to a URL using headless browser",
  "input_schema": {
    "type": "object",
    "properties": {
      "url": { "type": "string" },
      "wait_for": { "type": "string" },
      "timeout": { "type": "number" }
    },
    "required": ["url"]
  }
}

HTML Extraction Tools

{
  "name": "extract_html",
  "description": "Extract HTML content using CSS selectors",
  "input_schema": {
    "type": "object",
    "properties": {
      "selector": { "type": "string" },
      "attribute": { "type": "string" },
      "all": { "type": "boolean" }
    },
    "required": ["selector"]
  }
}

Screenshot and Visual Tools

For visual analysis tasks, you can leverage Claude's vision capabilities with MCP:

{
  "name": "take_screenshot",
  "description": "Capture screenshot of current page",
  "input_schema": {
    "type": "object",
    "properties": {
      "full_page": { "type": "boolean" },
      "element": { "type": "string" }
    }
  }
}

Advanced Use Cases

Dynamic Content Extraction

When working with JavaScript-heavy sites, you can combine Claude's intelligence with MCP's browser automation capabilities, similar to handling AJAX requests using Puppeteer:

async def extract_dynamic_content(url, data_description):
    """Use Claude to intelligently extract data from dynamic websites"""

    response = await claude_client.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=2048,
        tools=mcp_tools,
        messages=[{
            "role": "user",
            "content": f"""Navigate to {url} and extract {data_description}.
            Wait for all dynamic content to load before extracting.
            Return the data as structured JSON."""
        }]
    )

    # Claude will automatically:
    # 1. Navigate to the URL
    # 2. Wait for content to load
    # 3. Identify the right selectors
    # 4. Extract and structure the data

    return process_tool_results(response)

Intelligent Pagination Handling

Claude can understand pagination patterns and automate multi-page scraping:

async function scrapeAllPages(startUrl) {
  const response = await claude.messages.create({
    model: 'claude-3-5-sonnet-20241022',
    max_tokens: 4096,
    tools: mcpTools,
    messages: [{
      role: 'user',
      content: `Scrape all products from ${startUrl} across all pagination pages.
                Detect the pagination pattern and continue until no more pages exist.`
    }]
  });

  // Claude will intelligently:
  // - Identify pagination elements
  // - Click through pages or construct URLs
  // - Aggregate results
  // - Detect when pagination ends

  return await processResponse(response);
}

Error Handling and Retries

MCP servers can provide robust error handling similar to handling errors in Puppeteer:

def scrape_with_retry(url, max_retries=3):
    """Scrape with automatic retry logic through MCP"""

    for attempt in range(max_retries):
        try:
            response = claude_client.messages.create(
                model="claude-3-5-sonnet-20241022",
                max_tokens=1024,
                tools=mcp_tools,
                system="""If you encounter errors (timeouts, selectors not found, etc.),
                         analyze the error and try alternative approaches automatically.
                         Use different selectors, wait longer, or handle dynamic content.""",
                messages=[{
                    "role": "user",
                    "content": f"Extract data from {url}"
                }]
            )

            return process_response(response)

        except Exception as e:
            if attempt == max_retries - 1:
                raise
            time.sleep(2 ** attempt)  # Exponential backoff

Best Practices

1. Tool Definition Clarity

Define MCP tools with clear descriptions and schemas:

{
  "name": "scrape_table",
  "description": "Extract data from HTML tables. Returns array of objects with column headers as keys.",
  "input_schema": {
    "type": "object",
    "properties": {
      "table_selector": {
        "type": "string",
        "description": "CSS selector for the table element"
      },
      "headers_row": {
        "type": "number",
        "description": "Row index containing headers (0-based)",
        "default": 0
      }
    },
    "required": ["table_selector"]
  }
}

2. Rate Limiting and Quotas

Implement rate limiting in your MCP server:

const rateLimit = require('express-rate-limit');

const limiter = rateLimit({
  windowMs: 60 * 1000, // 1 minute
  max: 20, // 20 requests per minute
  message: 'Too many scraping requests, please try again later'
});

app.use('/tools', limiter);

3. Caching Responses

Cache frequently accessed data to reduce API calls:

from functools import lru_cache
import hashlib

@lru_cache(maxsize=100)
def cached_scrape(url: str, selector: str):
    """Cache scraping results for repeated requests"""
    cache_key = hashlib.md5(f"{url}{selector}".encode()).hexdigest()

    # Check cache first
    if cached_result := cache.get(cache_key):
        return cached_result

    # Perform scraping
    result = perform_scrape(url, selector)

    # Store in cache with TTL
    cache.set(cache_key, result, ttl=3600)
    return result

4. Security Considerations

Always validate inputs and sanitize URLs:

from urllib.parse import urlparse

def validate_scraping_request(url: str, allowed_domains: list):
    """Validate scraping requests for security"""
    parsed = urlparse(url)

    if parsed.scheme not in ['http', 'https']:
        raise ValueError("Invalid URL scheme")

    if parsed.netloc not in allowed_domains:
        raise ValueError("Domain not in allowlist")

    return True

Monitoring and Debugging

Logging Tool Calls

Track all tool executions for debugging:

class MCPServerWithLogging {
  async callTool(toolName, toolInput) {
    console.log(`[${new Date().toISOString()}] Tool called: ${toolName}`);
    console.log('Input:', JSON.stringify(toolInput, null, 2));

    const startTime = Date.now();

    try {
      const result = await this.executeTool(toolName, toolInput);
      const duration = Date.now() - startTime;

      console.log(`Tool completed in ${duration}ms`);
      console.log('Result:', JSON.stringify(result, null, 2));

      return result;
    } catch (error) {
      console.error(`Tool failed: ${error.message}`);
      throw error;
    }
  }
}

Performance Metrics

Track API usage and costs:

class ClaudeAPIMetrics:
    def __init__(self):
        self.total_tokens = 0
        self.total_requests = 0
        self.tool_calls = {}

    def track_request(self, response):
        self.total_tokens += response.usage.input_tokens
        self.total_tokens += response.usage.output_tokens
        self.total_requests += 1

        for content in response.content:
            if content.type == "tool_use":
                tool_name = content.name
                self.tool_calls[tool_name] = self.tool_calls.get(tool_name, 0) + 1

    def get_summary(self):
        return {
            "total_requests": self.total_requests,
            "total_tokens": self.total_tokens,
            "tool_calls": self.tool_calls,
            "estimated_cost": self.calculate_cost()
        }

Conclusion

The Claude API combined with MCP servers creates a powerful, flexible architecture for web scraping and data extraction. By leveraging Claude's intelligence and MCP's tool execution capabilities, developers can build sophisticated scraping systems that adapt to changing websites, handle complex scenarios, and extract structured data efficiently.

This integration enables you to move beyond static scraping scripts to intelligent agents that can understand context, make decisions, and handle edge cases automatically. Whether you're building data pipelines, monitoring competitors, or conducting research, the Claude API with MCP servers provides a robust foundation for your web scraping needs.