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D4Vinci / MCP Servers · Registry oficial

Scrapling

🕷️ A Web Scraping framework that handles everything from a single request to a full-scale crawl! Don't be shy, join here: https://discord.gg/EMgGbDceNQ and follow here for daily tips and tricks: https://x.com/Scrapling_dev

  • 89.3k estrellas
  • 9.2k forks
  • 16 issues abiertos
  • Lenguaje: Python
  • Licencia: BSD-3-Clause
  • actualizado

Instalación

local · uvx (python)
Claude Code CLI
claude mcp add scrapling -- uvx scrapling
  1. Run the command above in your terminal (Claude Code), or paste the JSON config into claude_desktop_config.json (Claude Desktop).
  2. Replace any <placeholder> values with your API keys or paths.
  3. Restart Claude. The MCP server and its tools appear automatically.

Package name inferred from the repository name. Verify it exists on PyPI, or clone https://github.com/D4Vinci/Scrapling and follow its README.

Nota editorial

Scrapling is a Python web scraping framework that exposes its capabilities as an MCP server, allowing Claude and other MCP-compatible clients to perform web data extraction directly within AI workflows. The library provides four fetcher classes, Fetcher, AsyncFetcher, StealthyFetcher, and DynamicFetcher, each suited to different scraping scenarios ranging from simple HTTP requests to full browser-based sessions that bypass anti-bot protections like Cloudflare Turnstile. A standout feature is its adaptive element relocation system: selectors saved with auto_save=True are stored in a local database, and if a site's HTML structure changes, passing adaptive=True causes the parser to find the same elements by their previously learned signatures rather than failing. The framework also includes a Spider class for concurrent multi-session crawls with pause/resume support and automatic proxy rotation. Via its MCP server interface, Claude Desktop and Claude Code users can invoke scraping, crawling, and data extraction tasks directly through the model, making it relevant to developers, data analysts, and researchers who need live web data integrated into AI-driven pipelines.

README

Resumen de MCP Servers

README completo en GitHub

Effortless Web Scraping for the Modern Web

Selection methods · Fetchers · Spiders · Proxy Rotation · CLI · MCP

Scrapling is a Python Web Scraping framework that brings fast HTTP requests, stealth browsers, powerful parsing, and concurrent crawling into one library.

Scrape static pages, render JavaScript, interact with websites, and extract data from the API calls behind them. Build crawlers with reusable sessions, proxy rotation, automatic retries, pause/resume, and built-in data exports.

Use it from Python, the terminal, or your AI agent through its MCP server and Agent Skill. From extracting a few fields to collecting a whole website as clean Markdown, Scrapling covers the full scraping workflow.

What you can do with Scrapling

  • Fetch & interact: Fast HTTP requests, stealth browsers, JavaScript rendering, Cloudflare Turnstile/Interstitial handling, persistent sessions, and background API capture.

  • Parse & extract: CSS, XPath, text and regex search, adaptive element tracking, similar-element matching, and clean Markdown output.

  • Crawl & export: Concurrent spiders, proxy rotation, retries, AutoThrottle, pause/resume, streaming, ready-made templates, and JSON/JSONL/CSV/XML exports.

  • Scrape with AI or from your terminal: An MCP server for AI agents to browse and extract data, an Agent Skill, CLI commands, and an interactive shell.

Start with a single page:

from scrapling.fetchers import Fetcher

page = Fetcher.get('https://quotes.toscrape.com/')
quotes = page.css('.quote .text::text').getall()
print(quotes)

Or scale up with a spider:

from scrapling.spiders import Spider, Response

class QuotesSpider(Spider):
    name = "quotes"
    start_urls = ["https://quotes.toscrape.com/"]

    async def parse(self, response: Response):
        for quote in response.css('.quote'):
            yield {
                "text": quote.css('.text::text').get(),
                "author": quote.css('.author::text').get(),
            }

result = QuotesSpider().start()
print(len(result.items))
result.items.to_json("quotes.json")

This prints 10 and saves the quotes and authors to quotes.json.

Platinum Sponsors

NodeMaven - The most efficient proxy provider for Web Scraping and Automation with the Highest Quality IP on the market. Use code SCRAPLING35 for 35% discount. Scrapling handles Cloudflare Turnstile. For enterprise-grade protection, Hyper Solutions provides API endpoints that generate valid antibot tokens for Akamai, DataDome, Kasada, and Incapsula. Simple API calls, no browser automation required. Evomi

Topics
aiai-scrapingautomationcrawlercrawlingcrawling-pythondatadata-extractionmcpmcp-serverplaywrightpythonscrapingselectorsstealthweb-scraperweb-scrapingweb-scraping-pythonwebscrapingxpath
Preguntas frecuentes

Lo que la gente pregunta sobre Scrapling

¿Qué es D4Vinci/Scrapling?

D4Vinci/Scrapling es mcp servers para el ecosistema de Claude AI. 🕷️ A Web Scraping framework that handles everything from a single request to a full-scale crawl! Don't be shy, join here: https://discord.gg/EMgGbDceNQ and follow here for daily tips and tricks: https://x.com/Scrapling_dev Tiene 89.3k estrellas en GitHub y su última actualización registrada es del 2026-10-22.

¿Cómo se instala Scrapling?

Puedes instalar Scrapling clonando el repositorio (https://github.com/D4Vinci/Scrapling) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.

¿Es seguro usar D4Vinci/Scrapling?

Nuestro agente de seguridad ha analizado D4Vinci/Scrapling y le ha asignado un Trust Score de 100/100 (tier: Verified). Revisa el desglose completo de comprobaciones superadas y flags en esta página.

¿Quién mantiene D4Vinci/Scrapling?

D4Vinci/Scrapling es mantenido por D4Vinci. La última actividad registrada en GitHub es del 2026-10-22, con 16 issues abiertos.

¿Hay alternativas a Scrapling?

Sí. En ClaudeWave puedes explorar mcp servers similares en /categories/mcp, ordenados por popularidad o actividad reciente.

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