El servidor ZenRows MCP (Model Context Protocol) es la forma estándar en que los sistemas de IA utilizan ZenRows. Una única conexión proporciona a tu asistente de IA, agente o aplicación acceso en tiempo real a cualquier sitio web.
📚 Documentación completa: docs.zenrows.com/integrations/mcp/mcp-overview
Accede a sitios que normalmente bloquean bots. Obtén acceso a cualquier sitio web a escala sin ser bloqueado por sistemas anti-bot.
Infraestructura de extracción gestionada. La rotación de proxies, la orquestación de navegadores headless, la evasión anti-bot y la gestión de sesiones se ejecutan en la infraestructura de ZenRows.
Conéctalo a cualquier IA que ya utilices. Funciona con cualquier cliente MCP, incluyendo asistentes de IA, marcos de trabajo de agentes, SDKs de IA, plugins de IDE y aplicaciones personalizadas.
Lenguaje sencillo, sin código de extracción. Describe la tarea de forma natural y la IA elegirá la herramienta adecuada. Sin selectores, sin gestión de proxies, sin ajustes anti-bot.
Related MCP server: defuddle-mcp
ZenRows MCP admite dos opciones de transporte. Ambas exponen el mismo conjunto de herramientas y capacidades. Elige la que mejor se adapte a tu cliente.
Utiliza el servidor ZenRows MCP alojado cuando tu aplicación de IA llame directamente a una API de LLM. El servidor se ejecuta en la infraestructura de ZenRows, por lo que no hay nada que instalar, configurar o actualizar.
URL del servidor:
https://mcp.zenrows.com/mcpTransporte: HTTP transmitible (Streamable)
Autenticación: Basada en OAuth. Pasa tu clave de API de ZenRows como un token Bearer en el encabezado Authorization en cada solicitud.
Authorization: Bearer YOUR_ZENROWS_API_KEYLa mayoría de los clientes MCP aceptan esto a través de un campo abreviado authorization en la configuración de la herramienta y lo reenvían automáticamente como el token Bearer. Algunos clientes utilizan un campo headers de formato libre en su lugar. Ambos enfoques funcionan.
import os
from openai import OpenAI
ZENROWS_API_KEY = os.environ["ZENROWS_API_KEY"]
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
response = client.responses.create(
model="gpt-5",
tools=[
{
"type": "mcp",
"server_label": "zenrows",
"server_description": "Web scraping MCP server for accessing live web content.",
"server_url": "https://mcp.zenrows.com/mcp",
"authorization": ZENROWS_API_KEY,
"require_approval": "never",
}
],
input="Visit https://news.ycombinator.com/ and summarize the three most recent posts.",
)
print(response.output_text)Para ver el tutorial completo con ejemplos específicos de cada marco de trabajo, consulta la documentación del servidor MCP remoto.
Utiliza la configuración stdio local cuando tu cliente MCP ejecute el servidor como un subproceso local en lugar de llamar a una URL remota. Esta es la configuración estándar para herramientas de IA de escritorio y plugins de IDE, incluyendo Claude Desktop, Claude Code, Cursor, Windsurf, VS Code, Zed y los IDEs de JetBrains.
Paquete: @zenrows/mcp en npm
Autenticación: Clave de API a través de la variable de entorno ZENROWS_API_KEY.
Requisitos: Node.js instalado (para que npx funcione).
Configuración:
{
"mcpServers": {
"zenrows": {
"command": "npx",
"args": ["-y", "@zenrows/mcp"],
"env": {
"ZENROWS_API_KEY": "YOUR_ZENROWS_API_KEY"
}
}
}
}La ubicación exacta de esta configuración varía según el cliente. Consulta las guías de configuración por cliente para conocer la ruta del archivo para tu cliente.
El ZenRows MCP expone dos familias de herramientas:
scrape: extracción de solicitud única que devuelve Markdown, texto plano, HTML, JSON, PDF o captura de pantalla. Respaldado por la API de Universal Scraper.
browser_*: más de 30 herramientas para la automatización completa del navegador, incluyendo navegación, clics, relleno de formularios, ejecución de JavaScript, cookies, pestañas y sesiones persistentes. Respaldado por el Navegador de Extracción (Scraping Browser).
La IA selecciona la herramienta adecuada a partir de tu prompt. No llamas a las herramientas directamente en el código.
Consulta la referencia completa de herramientas para ver cada herramienta, parámetro y valor de retorno.
git clone https://github.com/ZenRows/zenrows-mcp
cd zenrows-mcp
npm install
cp .env.example .env # Add your API key
npm run dev # Run with .env loaded (requires Node.js 20.6+)
npm run build # Compile to dist/
npm run inspect # Open the MCP inspector UILas solicitudes de extracción (pull requests) y los problemas (issues) son bienvenidos.
Scrape any webpage and return its content using ZenRows.
Use this tool to fetch webpage content for analysis. By default it returns clean markdown, which is ideal for LLM processing.
When to enable options:
js_render: page uses React/Vue/Angular, loads content dynamically, or content appears missing on the first attempt
premium_proxy: site returns 403/blocked errors even with js_render enabled
wait_for: specific content loads after initial render (requires js_render)
css_extractor: you only need specific elements, not the whole page
autoparse: structured data pages like products or articles
Examples: Basic: { url: "https://example.com" } Dynamic: { url: "https://spa.com", js_render: true } Protected:{ url: "https://protected.com", js_render: true, premium_proxy: true } Extract: { url: "https://shop.com", css_extractor: '{"title":"h1","price":".price"}' }
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The webpage URL to scrape | |
| js_render | No | Enable JavaScript rendering via headless browser. Required for SPAs (React, Vue, Angular) and pages that load content dynamically. | |
| premium_proxy | No | Use premium residential proxies to bypass anti-bot protection. Required for heavily protected sites. Implies higher credit cost. | |
| proxy_country | No | Country for geo-targeted scraping. ISO 3166-1 alpha-2 code (e.g. 'US', 'GB', 'DE'). Requires premium_proxy=true. | |
| response_type | No | Output format. 'markdown' (default) preserves structure and is ideal for LLMs. 'plaintext' strips all formatting for pure text extraction. 'pdf' returns a PDF of the page. 'html' returns the raw HTML source (omits the response_type param; ZenRows default). Ignored when autoparse, css_extractor, outputs, or screenshot params are set. | markdown |
| autoparse | No | Automatically extract structured data from the page into JSON. Best for product pages, articles, and listings. | |
| css_extractor | No | Extract specific elements using CSS selectors. JSON object mapping names to selectors, e.g. '{"title":"h1","price":".price-tag"}'. Returns JSON instead of full page content. | |
| wait_for | No | CSS selector to wait for before capturing. Use when key content loads after the initial page render. Requires js_render=true. | |
| wait | No | Milliseconds to wait after page load before capturing content. Max 30000 (30s). Requires js_render=true. | |
| js_instructions | No | JSON array of browser interactions to run before scraping. Requires js_render=true. Example: [{"click":"#load-more"},{"wait":1000},{"wait_for":".results"}] | |
| outputs | No | Comma-separated list of data types to extract as structured JSON. Available: emails, headings, links, menus, images, videos, audios. Use '*' for all types. Returns JSON instead of full page content. | |
| screenshot | No | Capture an above-the-fold screenshot of the page. Returns an image instead of text content. Useful for visual verification or debugging. | |
| screenshot_fullpage | No | Capture a full-page screenshot including content below the fold. Returns an image instead of text content. | |
| screenshot_selector | No | Capture a screenshot of a specific element using a CSS selector. Example: ".product-card". Returns an image instead of text content. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds valuable behavioral context: default markdown output ideal for LLMs, and crucially explains that certain parameters (css_extractor, autoparse, outputs, screenshot) change the return type from text to JSON or images. This output-switching behavior is not captured in annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with clear information hierarchy: purpose statement, default behavior, conditional options guide, and examples. Every section earns its place. Examples section is slightly verbose but appropriate for a 14-parameter tool where syntax matters. Good use of formatting (bullet points, code blocks).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with 14 parameters and no output schema, description adequately explains return value variations (markdown default vs JSON vs images depending on params). Covers the ZenRows-specific options (premium_proxy credit cost mentioned in schema, wait_for interactions explained). Could mention error handling or rate limits, but sufficient for invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, establishing baseline 3. Description adds significant value via the 'When to enable options' section which provides contextual semantics for when to use parameters (e.g., 'page uses React/Vue/Angular' triggers js_render). The concrete examples demonstrate parameter interactions and valid value formats (e.g., CSS selector JSON syntax).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Opens with specific verb+resource ('Scrape any webpage') and identifies the underlying service ('using ZenRows'). Clearly states default output format ('clean markdown') and primary use case ('fetch webpage content for analysis'). No siblings to differentiate from, but scope is precisely defined.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Contains explicit 'When to enable options' section that maps specific technical conditions (React/Vue/Angular, 403 errors, delayed content loading) to parameter usage. Provides concrete decision trees for selecting js_render, premium_proxy, and other options. Includes practical JSON examples showing parameter combinations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
With only one tool, there is no possibility of confusion or overlap between tools. The single 'scrape' tool has a clear, distinct purpose of fetching webpage content.
There is only one tool name, so consistency is inherently perfect. The name 'scrape' follows a clear verb-based pattern appropriate for its function.
A single tool is too few for most server purposes, as it limits functionality and flexibility. While scraping is a focused domain, having only one tool feels thin and may not cover related needs like batch processing or error handling.
The tool covers basic webpage scraping with options for dynamic content and proxies, but there are notable gaps. Missing operations might include checking scrape status, managing sessions, or handling rate limits, which could lead to agent workarounds or failures in complex scenarios.
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If you are the server author, to access and configure the admin panel.
One MCP for the Web. Easily search, crawl, navigate, and extract websites without getting blocked.…
Scrapingdog MCP — wraps Scrapingdog (scrapingdog.com), a proxy-based web
Firecrawl MCP — wraps the Firecrawl API (firecrawl.dev) for web
Turn any URL into clean Markdown and structured data. Scrape, crawl, search and extract.
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