Zenrows MCP

Zenrows MCP Server

The Zenrows MCP (Model Context Protocol) server is the standard way AI systems use Zenrows' web data infrastructure. A single connection gives your AI assistant, agent, or application reliable, real-time access to the live web, including the protected web.

📚 Full documentation: docs.zenrows.com/mcp/overview

Why Zenrows MCP

• Reach sites that normally block bots. Get reliable access to protected sites at scale, without building anti-bot handling yourself.
• Managed web data infrastructure. Proxy rotation, headless browser orchestration, anti-bot handling, and session management run on Zenrows' infrastructure.
• Plug into any AI you already use. Works with any MCP client, including AI assistants, agent frameworks, AI SDKs, IDE plugins, and custom applications.
• Plain English, no scraping code. Describe the task naturally and the AI picks the right tool. No selectors, no proxy management, no anti-bot tuning.

Quick start

Zenrows MCP supports two transport options. Both expose the same set of tools and capabilities. Pick the one that fits your client.

What works without a key:

• Local (`npx @zenrows/mcp`): no signup, no key. The first tool call creates a Free account (5,000 credits/month) that you can claim later, keeping the key and its usage. Opt out with ZENROWS_AUTO_SIGNUP=false. In CI, set ZENROWS_API_KEY as a secret or persist ZENROWS_HOME between runs.
• Remote (`https://mcp.zenrows.com/mcp`): initialize and tools/list answer without a key; tool calls need one (OAuth in clients that support it, or a Bearer key). The remote server never creates accounts by itself. A headless agent can create a Free account with POST https://app.zenrows.com/api/agent/signup and use the returned apiKey as the Bearer token. The endpoint may first answer 428 with a short question; POST {"challenge_id": "...", "answer": "..."} back to the same URL to get the key.

Remote MCP server

Use the hosted Zenrows MCP server when your AI application calls an LLM API directly. The server runs on Zenrows' infrastructure, so there is nothing to install, configure, or update.

Server URL:

https://mcp.zenrows.com/mcp

Transport: Streamable HTTP

Authentication: OAuth or API key as Bearer token. Pass your Zenrows API key in the Authorization header on every request (or complete OAuth in clients that support it).

Authorization: Bearer YOUR_ZENROWS_API_KEY

Most MCP clients accept this through an authorization shorthand field on the tool config and forward it as the Bearer token automatically. Some clients use a free-form headers field instead. Either approach works.

Remote MCP does not auto-create accounts. initialize and tools/list work without a key; a keyless tool call gets a 401 whose body names the signup endpoint (agent_signup). Use OAuth “Create Free account” in the client, pass an existing API key, or, from a headless agent, POST https://app.zenrows.com/api/agent/signup (anonymous; returns apiKey, or first a 428 question to answer with challenge_id and answer; per-network limits apply) and retry with Authorization: Bearer .

Example: OpenAI Responses API

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)

For the full walkthrough with framework-specific examples, see the Remote MCP server docs.

Local MCP server

Use the local stdio configuration when your MCP client runs the server as a local subprocess instead of calling a remote URL. This is the standard setup for desktop AI tools and IDE plugins, including Claude Desktop, Claude Code, Cursor, Windsurf, VS Code, Zed, and JetBrains IDEs.

Package: @zenrows/mcp on npm

Authentication:

1. ZENROWS_API_KEY environment variable, or
2. Key previously stored in ~/.zenrows/secrets.json, or
3. Auto-signup (default): if neither is set, the server still starts, and the first tool call provisions a Free plan account via POST /api/agent/signup, persists the key + claim metadata under ~/.zenrows/ (secrets.json + account.json, mode 0600), and prints a claim URL on stderr. Zenrows may first ask a short question: the tool call then returns it, and the agent answers with the account_signup_answer tool, then retries. If signup is refused (for example the per-network cap in CI), that tool call returns an error saying when to retry; in CI, set ZENROWS_API_KEY as a secret or persist ZENROWS_HOME between runs. Opt out with ZENROWS_AUTO_SIGNUP=false: tool calls then return an error until a key is set. The signup request also sends a random per-machine id (shared with the Zenrows CLI) and a guess at the host agent (claude-code, cursor, …) so signups can be attributed; set ZENROWS_TELEMETRY=off to omit both.

Requirements: Node.js installed (for npx to work).

Configuration (with your own key):

{

"mcpServers": { "zenrows": { "command": "npx", "args": ["-y", "@zenrows/mcp"], "env": { "ZENROWS_API_KEY": "YOUR_ZENROWS_API_KEY" } } } }

Zero-config (auto-signup):

{

"mcpServers": { "zenrows": { "command": "npx", "args": ["-y", "@zenrows/mcp"] } } }

Optional: ZENROWS_CRAWL_API_BASE sets the Crawl API base URL for the crawl_* tools (default https://api.zenrows.com/v1). Set it only to point those tools at another deployment.

Optional: ZENROWS_BATCH_API_BASE sets the Batch API base URL for the batch_* tools (default https://async.api.zenrows.com/v1). Set it only to point those tools at another deployment.

The exact location of this config varies by client. See the per-client setup guides for the file path for your client.

Tools

The Zenrows MCP exposes these tool families:

Batch tools

A Batch job scrapes many URLs asynchronously. The usual flow: batch_create, then batch_wait until the run ends, batch_results to page through the rows, and batch_content to read one task's page.

• `batch_create` submits one closed job with all its tasks, up to 100,00