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What are the Limitations of MCP servers for Web Scraping?

While Model Context Protocol (MCP) servers offer a powerful framework for integrating web scraping capabilities into AI-assisted workflows, they come with several important limitations and constraints that developers need to understand. This article explores the technical challenges, performance bottlenecks, and practical restrictions you'll encounter when using MCP servers for web scraping at scale.

Performance and Scalability Limitations

1. Synchronous Request Processing

MCP servers typically handle requests synchronously, which creates a bottleneck when scraping multiple pages. Unlike dedicated scraping frameworks that can process hundreds of concurrent requests, MCP servers process one AI request at a time.

from mcp.server import Server
from mcp.types import Tool, TextContent
import httpx
from bs4 import BeautifulSoup

app = Server("web-scraper")

@app.call_tool()
async def call_tool(name: str, arguments: dict) -> list[TextContent]:
    # This executes sequentially for each AI request
    if name == "scrape_url":
        url = arguments["url"]
        async with httpx.AsyncClient() as client:
            response = await client.get(url)
            # Processing happens one request at a time
            return [TextContent(
                type="text",
                text=response.text
            )]

Impact: When scraping 100 pages, an MCP server might take 5-10 minutes, while a dedicated scraper could complete the same task in under a minute using parallel requests.

2. Memory Constraints

MCP servers run as separate processes and are subject to memory limitations, especially when handling large-scale scraping operations:

// MCP server processing large HTML documents
server.setRequestHandler("tools/call", async (request) => {
  const { name, arguments: args } = request.params;

  if (name === "scrape_bulk") {
    const urls = args.urls; // Array of 1000 URLs
    const results = [];

    // This can quickly exhaust available memory
    for (const url of urls) {
      const response = await axios.get(url);
      results.push(response.data); // Accumulating large HTML documents
    }

    // Memory usage can spike to several GB
    return {
      content: [{
        type: "text",
        text: JSON.stringify(results)
      }]
    };
  }
});

Workaround: Implement streaming or batch processing with intermediate storage:

import json
import tempfile

@app.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "scrape_bulk":
        urls = arguments["urls"]

        # Use temporary file to avoid memory issues
        with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
            for url in urls:
                async with httpx.AsyncClient() as client:
                    response = await client.get(url)
                    # Process and write immediately
                    data = extract_data(response.text)
                    f.write(json.dumps(data) + '\n')

            f.seek(0)
            return [TextContent(
                type="text",
                text=f"Results saved to {f.name}"
            )]

3. Timeout Limitations

MCP servers have built-in timeout constraints that can interrupt long-running scraping operations:

// Default timeout is typically 30-60 seconds
server.setRequestHandler("tools/call", async (request) => {
  if (name === "scrape_slow_site") {
    // This might timeout if the site is slow or requires
    // multiple navigation steps like handling browser sessions
    const browser = await puppeteer.launch();
    const page = await browser.newPage();

    await page.goto(args.url);
    await page.waitForSelector('.content', { timeout: 30000 });

    // If total execution exceeds MCP timeout, operation fails
    const data = await page.evaluate(() => {
      // Complex extraction logic
    });

    await browser.close();
    return { content: [{ type: "text", text: JSON.stringify(data) }] };
  }
});

Browser Automation Challenges

4. Limited Browser Lifecycle Management

Unlike standalone automation tools that can maintain persistent browser instances, MCP servers must create and destroy browser contexts for each request:

from playwright.async_api import async_playwright

@app.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "scrape_dynamic":
        # Browser launch overhead: 2-3 seconds per request
        async with async_playwright() as p:
            browser = await p.chromium.launch()
            page = await browser.new_page()

            await page.goto(arguments["url"])
            content = await page.content()

            # Browser must be closed, can't be reused
            await browser.close()

        return [TextContent(type="text", text=content)]

Performance Impact: Starting a new browser instance adds 2-5 seconds of overhead per request. For 100 pages, that's 3-8 minutes of pure overhead.

Alternative Approach: While MCP doesn't natively support persistent connections, you could implement a connection pool pattern, though this adds complexity:

class BrowserPool {
  constructor(size = 3) {
    this.size = size;
    this.browsers = [];
    this.available = [];
  }

  async initialize() {
    for (let i = 0; i < this.size; i++) {
      const browser = await puppeteer.launch();
      this.browsers.push(browser);
      this.available.push(browser);
    }
  }

  async acquire() {
    while (this.available.length === 0) {
      await new Promise(resolve => setTimeout(resolve, 100));
    }
    return this.available.pop();
  }

  release(browser) {
    this.available.push(browser);
  }
}

// Still limited by MCP's sequential request processing
const pool = new BrowserPool(3);
await pool.initialize();

5. Restricted Access to Advanced Browser Features

MCP servers may not expose all browser automation capabilities available in tools like Puppeteer for handling iframes or managing complex navigation flows:

// Limited capability for multi-step interactions
server.setRequestHandler("tools/call", async (request) => {
  if (name === "complex_scraping") {
    // Difficult to implement:
    // - Multi-tab workflows
    // - Complex user interaction sequences
    // - Session persistence across requests
    // - Cookie and storage management across AI interactions

    // Each MCP tool call is isolated
    const browser = await puppeteer.launch();
    const page = await browser.newPage();

    // Can't easily maintain state between AI requests
    await page.goto(args.url);
    // ... scraping logic
    await browser.close();
  }
});

Network and Connection Constraints

6. Proxy and Network Configuration Limitations

While you can configure proxies in MCP servers, managing proxy rotation and failure handling is more complex than in dedicated scraping frameworks:

import httpx
from itertools import cycle

# Proxy rotation is manual and error-prone
PROXIES = [
    "http://proxy1.example.com:8080",
    "http://proxy2.example.com:8080",
    "http://proxy3.example.com:8080",
]
proxy_pool = cycle(PROXIES)

@app.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "scrape_with_proxy":
        proxy = next(proxy_pool)

        try:
            async with httpx.AsyncClient(proxies={"http://": proxy}) as client:
                response = await client.get(arguments["url"])
                return [TextContent(type="text", text=response.text)]
        except httpx.ProxyError:
            # No automatic failover to next proxy
            # Each failure requires a new AI request
            return [TextContent(
                type="text",
                text=f"Proxy {proxy} failed. Please retry."
            )]

7. Rate Limiting Complexity

Implementing sophisticated rate limiting across multiple MCP server instances is challenging:

// Simple rate limiting works, but doesn't scale across instances
class RateLimiter {
  constructor(requestsPerSecond) {
    this.rate = requestsPerSecond;
    this.queue = [];
    this.processing = false;
  }

  async throttle(fn) {
    return new Promise((resolve) => {
      this.queue.push(async () => {
        const result = await fn();
        resolve(result);
      });
      this.process();
    });
  }

  async process() {
    if (this.processing || this.queue.length === 0) return;

    this.processing = true;
    const fn = this.queue.shift();
    await fn();

    setTimeout(() => {
      this.processing = false;
      this.process();
    }, 1000 / this.rate);
  }
}

// Works for single MCP server instance only
const limiter = new RateLimiter(2);

server.setRequestHandler("tools/call", async (request) => {
  return await limiter.throttle(async () => {
    // Scraping logic
  });
});

Data Processing and Storage Limitations

8. Limited Data Transformation Capabilities

MCP servers typically return data as text or JSON, which limits complex data processing pipelines:

@app.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "scrape_and_analyze":
        # Can scrape and parse
        async with httpx.AsyncClient() as client:
            response = await client.get(arguments["url"])
            soup = BeautifulSoup(response.text, 'html.parser')

            # Limited ability to:
            # - Store results in databases directly
            # - Perform complex aggregations
            # - Generate reports or visualizations
            # - Trigger downstream workflows

            data = [item.text for item in soup.select('.product')]

            # Must return simple text/JSON to AI
            return [TextContent(
                type="text",
                text=json.dumps(data)
            )]

9. No Built-in Caching or Deduplication

Unlike specialized scraping frameworks, MCP servers don't provide built-in mechanisms for caching results or avoiding duplicate requests:

// Must implement caching manually
const cache = new Map();

server.setRequestHandler("tools/call", async (request) => {
  const { name, arguments: args } = request.params;

  if (name === "fetch_html") {
    const cacheKey = `${args.url}:${JSON.stringify(args.headers)}`;

    if (cache.has(cacheKey)) {
      const cached = cache.get(cacheKey);
      if (Date.now() - cached.timestamp < 3600000) { // 1 hour
        return {
          content: [{
            type: "text",
            text: cached.data
          }]
        };
      }
    }

    const response = await axios.get(args.url);
    cache.set(cacheKey, {
      data: response.data,
      timestamp: Date.now()
    });

    return {
      content: [{
        type: "text",
        text: response.data
      }]
    };
  }
});

Error Handling and Reliability Issues

10. Limited Error Recovery

When an MCP server encounters an error, recovery options are limited compared to dedicated scraping frameworks:

@app.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "scrape_with_retry":
        max_retries = 3

        for attempt in range(max_retries):
            try:
                async with httpx.AsyncClient() as client:
                    response = await client.get(arguments["url"])
                    return [TextContent(type="text", text=response.text)]
            except Exception as e:
                if attempt == max_retries - 1:
                    # Final failure - AI must handle retry logic
                    return [TextContent(
                        type="text",
                        text=f"Failed after {max_retries} attempts: {str(e)}"
                    )]

                # Exponential backoff
                await asyncio.sleep(2 ** attempt)

Challenge: The AI must be aware of failures and explicitly retry, whereas dedicated scrapers can implement sophisticated retry logic with backoff strategies, circuit breakers, and automatic failover.

11. Monitoring and Debugging Constraints

MCP servers lack built-in monitoring and debugging tools that are standard in scraping frameworks:

// Limited visibility into scraping operations
server.setRequestHandler("tools/call", async (request) => {
  const { name, arguments: args } = request.params;

  // Basic logging to stderr (not visible to AI)
  console.error(`[${new Date().toISOString()}] Scraping ${args.url}`);

  try {
    const response = await axios.get(args.url);

    // No built-in metrics for:
    // - Success rates
    // - Response times
    // - Error frequencies
    // - Resource usage

    return {
      content: [{
        type: "text",
        text: response.data
      }]
    };
  } catch (error) {
    // Error details may not be fully accessible
    console.error(`Error scraping ${args.url}:`, error);

    return {
      content: [{
        type: "text",
        text: `Error: ${error.message}`
      }],
      isError: true
    };
  }
});

Security and Compliance Limitations

12. Limited Access Control

MCP servers have basic security features but lack fine-grained access control found in enterprise scraping solutions:

# Basic URL validation
ALLOWED_DOMAINS = ['example.com', 'trusted-site.org']

@app.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "scrape_url":
        from urllib.parse import urlparse

        url = arguments["url"]
        domain = urlparse(url).netloc

        # Simple allow/deny - no role-based access
        # No audit logging of who requested what
        # No quota management per user
        if not any(domain.endswith(d) for d in ALLOWED_DOMAINS):
            return [TextContent(
                type="text",
                text=f"Access denied: {domain} not in allowed list"
            )]

        # ... scraping logic

13. Compliance and Legal Constraints

MCP servers don't automatically handle compliance requirements like respecting robots.txt or GDPR:

// Manual robots.txt checking required
const robotsParser = require('robots-parser');

async function canScrape(url) {
  const parsedUrl = new URL(url);
  const robotsUrl = `${parsedUrl.protocol}//${parsedUrl.host}/robots.txt`;

  try {
    const response = await axios.get(robotsUrl);
    const robots = robotsParser(robotsUrl, response.data);

    // Must manually check for each request
    return robots.isAllowed(url, 'MCPBot');
  } catch (error) {
    // Assume allowed if robots.txt not found
    return true;
  }
}

server.setRequestHandler("tools/call", async (request) => {
  if (name === "ethical_scrape") {
    if (!await canScrape(args.url)) {
      return {
        content: [{
          type: "text",
          text: "Scraping not allowed by robots.txt"
        }],
        isError: true
      };
    }

    // Proceed with scraping
  }
});

Working Around MCP Limitations

Strategy 1: Hybrid Approach

Combine MCP servers with dedicated scraping infrastructure:

# MCP server acts as a coordinator
import requests

SCRAPING_API_URL = "https://api.webscraping.ai/html"

@app.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "scrape_url":
        # Delegate heavy lifting to specialized API
        response = requests.get(SCRAPING_API_URL, params={
            'url': arguments['url'],
            'api_key': 'YOUR_API_KEY',
            'js': arguments.get('js_rendering', False)
        })

        return [TextContent(
            type="text",
            text=response.text
        )]

Strategy 2: Task Queuing

For large-scale operations, use MCP to enqueue tasks rather than execute them directly:

const Bull = require('bull');
const queue = new Bull('scraping-jobs');

server.setRequestHandler("tools/call", async (request) => {
  if (name === "queue_scraping_job") {
    // Add job to queue instead of executing immediately
    const job = await queue.add({
      url: args.url,
      selectors: args.selectors
    });

    return {
      content: [{
        type: "text",
        text: `Job queued with ID: ${job.id}`
      }]
    };
  }

  if (name === "check_job_status") {
    const job = await queue.getJob(args.jobId);
    const state = await job.getState();

    if (state === 'completed') {
      return {
        content: [{
          type: "text",
          text: JSON.stringify(job.returnvalue)
        }]
      };
    }

    return {
      content: [{
        type: "text",
        text: `Job status: ${state}`
      }]
    };
  }
});

Strategy 3: Optimized for AI Use Cases

Design MCP servers specifically for AI-assisted scraping rather than general-purpose scraping:

@app.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "ai_extract":
        # Instead of returning full HTML, return pre-processed data
        async with httpx.AsyncClient() as client:
            response = await client.get(arguments["url"])
            soup = BeautifulSoup(response.text, 'html.parser')

            # Extract and structure relevant content
            structured_data = {
                'title': soup.find('h1').text if soup.find('h1') else '',
                'main_content': soup.select_one('article, main, .content'),
                'links': [a['href'] for a in soup.select('a[href]')],
                'images': [img['src'] for img in soup.select('img[src]')]
            }

            # Return concise, AI-ready data instead of raw HTML
            return [TextContent(
                type="text",
                text=json.dumps(structured_data, indent=2)
            )]

When to Use MCP Servers Despite Limitations

MCP servers are ideal for:

  1. AI-Assisted Research: Ad-hoc data gathering guided by AI reasoning
  2. Prototype Development: Quick testing of scraping approaches with AI feedback
  3. Small-Scale Operations: Scraping dozens of pages, not thousands
  4. Interactive Workflows: When human oversight and AI decision-making are valuable
  5. Integration with AI Tools: When scraping is one part of a larger AI-powered workflow

When to Use Dedicated Scraping Solutions

Use specialized scraping frameworks or APIs (like WebScraping.AI) when you need:

  1. High-Volume Scraping: Thousands or millions of pages
  2. Production Reliability: 99.9% uptime requirements
  3. Advanced Anti-Bot Evasion: Residential proxies, browser fingerprinting, CAPTCHA solving
  4. Complex Workflows: Multi-step navigation similar to navigating to different pages using Puppeteer
  5. Enterprise Features: SLA guarantees, compliance tools, dedicated support

Conclusion

MCP servers provide a valuable bridge between AI assistants and web scraping capabilities, but they have significant limitations in performance, scalability, and features compared to dedicated scraping solutions. Understanding these constraints is crucial for making informed architectural decisions.

For AI-assisted scraping workflows where flexibility and intelligence matter more than raw performance, MCP servers excel. However, for production-scale scraping, consider using MCP servers as coordinators that delegate to specialized scraping infrastructure rather than performing all scraping operations directly.

By recognizing these limitations and designing around them—through hybrid architectures, task queuing, or strategic use of external APIs—you can leverage the strengths of both MCP servers and dedicated scraping solutions to build robust, intelligent data extraction systems.

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