Connect your AI agent to Zenrows

Connect your AI assistant, coding agent or application to Zenrows once, and it can read any page, extract structured data, crawl a site, run large jobs and drive a real browser on your behalf. Every recipe in this Academy includes an Ask your AI agent prompt you can paste as soon as you’re connected. Uses: Zenrows MCP server, Zenrows CLI, Zenrows docs for agents

Choose your path

You use	Connect with	Time
Claude (web, desktop or mobile)	The Zenrows connector, signed in with OAuth	1 minute
Claude Code, Cursor, VS Code, Windsurf, Codex, Gemini CLI	The MCP server, plus the CLI’s skills for your agent	2 minutes
Your own agent on an LLM API	The remote MCP server URL as a tool	5 minutes
A coding agent writing Zenrows code	Zenrows docs as context (docs MCP server, `llms.txt`)	1 minute

You need a Zenrows account and, for API-key setups, your key from the dashboard.

Connect Claude

Claude connects to the hosted Zenrows MCP server and signs you in with OAuth, so there is no key to copy. Follow Claude for the web, desktop and mobile apps. In Claude Code, install the Zenrows plugin. It adds the MCP server and the Zenrows skills that teach Claude which Zenrows tool fits each task:

Claude Code

/plugin install zenrows --marketplace ZenRows/claude-zenrows

Then run /mcp, select zenrows and sign in. To use an API key instead, add the local server:

Terminal

claude mcp add zenrows -e ZENROWS_API_KEY=YOUR_ZENROWS_API_KEY -- npx -y @zenrows/mcp

Connect any MCP client

Every MCP client takes the same two settings: the server URL and your key as a Bearer token.

Field	Value
Server URL	`https://mcp.zenrows.com/mcp`
Transport	Streamable HTTP
Header	`Authorization: Bearer YOUR_ZENROWS_API_KEY`

Clients that run servers locally use the @zenrows/mcp package instead:

JSON

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

Step-by-step guides with each client’s config file path: Cursor, VS Code, Windsurf, Zed, JetBrains, OpenCode, Claude Desktop. Both servers expose the same tools: scrape and extract for single pages, crawl_* for whole sites, batch_* for URL lists, and the browser_* tools for Browser. See the tool reference.

Set up your coding agent with the CLI

The Zenrows CLI prepares a project for your coding agents in one command: it signs you in, prints the MCP config for each agent, and installs Zenrows skills, the playbooks that tell an agent which primitive and parameters fit a task.

Terminal

npm install -g @zenrows/cli
zenrows init --agents claude-code,cursor,codex

To add one client later, zenrows plugin install <client> installs the MCP config and every skill together. Supported clients: claude-code, cursor, vscode, windsurf, gemini, codex, opencode, generic. See Skills and MCP setup from the CLI.

Connect your own agent

Agents built on an LLM API take the remote server as a tool definition. With the OpenAI Responses API:

Python

import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

response = client.responses.create(
    model="gpt-5",
    tools=[
        {
            "type": "mcp",
            "server_label": "zenrows",
            "server_url": "https://mcp.zenrows.com/mcp",
            "authorization": os.environ["ZENROWS_API_KEY"],
            "require_approval": "never",
        }
    ],
    input="Read https://www.scrapingcourse.com/ecommerce/ and list the first five products with prices.",
)
print(response.output_text)

Framework guides: Anthropic API, Claude Agent SDK, OpenAI Agents SDK, Google ADK, LangChain, LlamaIndex, Mastra, CrewAI. To build the agent loop yourself, follow Build a research agent with live web data.

An agent that has no Zenrows account yet can buy and call Fetch and Extract on its own, paying per request. See Agentic payments.

Give your agent the Zenrows docs

When a coding agent writes Zenrows code, give it the docs as context so it uses exact parameter names:

Docs MCP server: add https://docs.zenrows.com/mcp to your client. It gives the agent a search_zenrows tool over these docs.
llms.txt: docs.zenrows.com/llms.txt indexes every page, and llms-full.txt holds the full text.
Any page as markdown: add .md to a page URL, or use the Copy page menu at the top of every page to send it to Claude, ChatGPT or Perplexity.
OpenAPI: Fetch, Extract and Batch are described in OpenAPI 3.1 at https://www.zenrows.com/openapi.json.

Check the connection

Paste this prompt into your assistant:

Use Zenrows to check my account usage, then read https://www.scrapingcourse.com/ecommerce/
and tell me how many products are on the first page.

Your agent calls account_usage and scrape, and answers with your plan usage and the product count. You can also confirm the server from a terminal: a tools/list call returns every Zenrows tool.

cURL

curl https://mcp.zenrows.com/mcp \
  -H "Authorization: Bearer YOUR_ZENROWS_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'

Example output

An illustrative reply to the prompt above. Your agent’s wording and your usage figures differ:

Your Zenrows account is active. You've used 1,240 of your plan's credits this period.

The first page of https://www.scrapingcourse.com/ecommerce/ lists 16 products,
starting with Abominable Hoodie ($69.00), Adrienne Trek Jacket ($57.00) and
Aeon Capri ($48.00).

Troubleshooting

The assistant answers from memory instead of the page: name the tool in the prompt, for example “Use Zenrows to read…”.
401 from the server: the Bearer token isn’t a valid Zenrows API key. Copy the key again from the dashboard, with the Bearer prefix.
The local server doesn’t start: npx needs Node.js 20.6 or later on your PATH.

Prompts that put Zenrows MCP to work

A prompt library for every primitive and workflow.

Let your AI assistant browse with Browser tools

Drive Zenrows Browser with plain-language instructions.

Build a research agent with live web data

Wire Zenrows into your own agent loop.

Automate scraping from your terminal with the CLI

Script fetch, extract, crawl and batch with the CLI.

Start Here

Foundations Course

Fetch Recipes

Extract Recipes

Crawl Recipes

Batch Recipes

Browser Recipes

Pipelines

Use Cases

By Surface

Production

Connect your AI agent to Zenrows

Choose your path

Connect Claude

Connect any MCP client

Set up your coding agent with the CLI

Connect your own agent

Give your agent the Zenrows docs

Check the connection

Example output

Troubleshooting

Prompts that put Zenrows MCP to work

Let your AI assistant browse with Browser tools

Build a research agent with live web data

Automate scraping from your terminal with the CLI

​Choose your path

​Connect Claude

​Connect any MCP client

​Set up your coding agent with the CLI

​Connect your own agent

​Give your agent the Zenrows docs

​Check the connection

​Example output

​Troubleshooting

​Related recipes

Prompts that put Zenrows MCP to work

Let your AI assistant browse with Browser tools

Build a research agent with live web data

Automate scraping from your terminal with the CLI

Choose your path

Connect Claude

Connect any MCP client

Set up your coding agent with the CLI

Connect your own agent

Give your agent the Zenrows docs

Check the connection

Example output

Troubleshooting

Related recipes