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How to integrate Zenrows MCP with Mastra AI

Connect Mastra AI to Zenrows MCP. Download a pdf of this news article, extract plain text from the given webpage, and more using natural language, with authentication handled for you.

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Zenrows

Api Key

ZenRows is a web scraping API that helps you gather structured data from dynamic and protected websites. It makes extracting web data easy by bypassing CAPTCHAs and anti-bot measures.

14 Tools

Managed auth

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We manage OAuth, API keys, token refresh, and scopes — you just build.

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Introduction

This guide walks you through connecting Zenrows to Mastra AI using the Composio tool router. By the end, you'll have a working Zenrows agent that can scrape pages as HTML or text, extract data with CSS selectors, take screenshots, save PDFs, and check your usage through natural language commands.

This guide will help you understand how to give your Mastra AI agent real control over a Zenrows account through Composio's Zenrows MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Zenrows with

Claude Code Claude Cowork OpenAI Agents SDK Claude Agent SDK Muse Codex OpenClaw Hermes Atomic Agent Google ADK LangChain Vercel AI SDK LlamaIndex CrewAI Pydantic AI AutoGen

TL;DR

Here's what you'll learn:

Set up your environment so Mastra, OpenAI, and Composio work together
Create a Tool Router session in Composio that exposes Zenrows tools
Connect Mastra's MCP client to the Composio generated MCP URL
Fetch Zenrows tool definitions and attach them as a toolset
Build a Mastra agent that can reason, call tools, and return structured results
Run an interactive CLI where you can chat with your Zenrows agent

What is Mastra AI?

Mastra AI is a TypeScript framework for building AI agents with tool support. It provides a clean API for creating agents that can use external services through MCP.

Key features include:

MCP Client: Built-in support for Model Context Protocol servers
Toolsets: Organize tools into logical groups
Step Callbacks: Monitor and debug agent execution
OpenAI Integration: Works with OpenAI models via @ai-sdk/openai

What is the Zenrows MCP server, and what's possible with it?

The ZenRows MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your ZenRows account. It provides structured and secure access to web scraping and account usage data, so your agent can extract webpage content, collect structured data, capture screenshots, generate PDFs, and monitor usage on your behalf.

Webpage content extraction: Have your agent retrieve raw HTML or clean plain text from a given webpage.
Structured web data collection: Let the agent automatically parse a webpage, extract selected elements with CSS selectors, or collect detailed Walmart product information.
Page screenshots and PDFs: Direct your agent to capture a screenshot of a webpage or generate a PDF from its scraped content.
Scraping diagnostics and sessions: Instruct your agent to check original website status codes and response headers, use compressed responses, or maintain the same IP across related requests.
Account capacity monitoring: Have your agent review ZenRows API usage, current concurrency, and account limits before running scraping tasks.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step09 STEPS

Prerequisites

Before starting, make sure you have:

Node.js 18 or higher
A Composio account with an active API key
An OpenAI API key
Basic familiarity with TypeScript

Getting API Keys for OpenAI and Composio

OpenAI API Key

Go to the OpenAI dashboard and create an API key.
You need credits or a connected billing setup to use the models.
Store the key somewhere safe.

Composio API Key

Log in to the Composio dashboard.
Go to Settings and copy your API key.
This key lets your Mastra agent talk to Composio and reach Zenrows through MCP.

Install dependencies

bash

npm install @composio/core @mastra/core @mastra/mcp @ai-sdk/openai dotenv

Install the required packages.

What's happening:

@composio/core is the Composio SDK for creating MCP sessions
@mastra/core provides the Agent class
@mastra/mcp is Mastra's MCP client
@ai-sdk/openai is the model wrapper for OpenAI
dotenv loads environment variables from .env

Set up environment variables

bash

COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

COMPOSIO_API_KEY authenticates your requests to Composio
COMPOSIO_USER_ID tells Composio which user this session belongs to
OPENAI_API_KEY lets the Mastra agent call OpenAI models

Import libraries and validate environment

typescript

import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey as string,
});

What's happening:

dotenv/config auto loads your .env so process.env.* is available
openai gives you a Mastra compatible model wrapper
Agent is the Mastra agent that will call tools and produce answers
MCPClient connects Mastra to your Composio MCP server
Composio is used to create a Tool Router session

Create a Tool Router session for Zenrows

typescript

async function main() {
  const session = await composio.create(
    composioUserID as string,
    {
      toolkits: ["zenrows"],
    },
  );

  const composioMCPUrl = session.mcp.url;
  console.log("Zenrows MCP URL:", composioMCPUrl);

What's happening:

create spins up a short-lived MCP HTTP endpoint for this user
The toolkits array contains "zenrows" for Zenrows access
session.mcp.url is the MCP URL that Mastra's MCPClient will connect to

Configure Mastra MCP client and fetch tools

typescript

const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      nasdaq: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

console.log("Fetching MCP tools from Composio...");
const composioTools = await mcpClient.getTools();
console.log("Number of tools:", Object.keys(composioTools).length);

What's happening:

MCPClient takes an id for this client and a list of MCP servers
The headers property includes the x-api-key for authentication
getTools fetches the tool definitions exposed by the Zenrows toolkit

Create the Mastra agent

typescript

const agent = new Agent({
    name: "zenrows-mastra-agent",
    instructions: "You are an AI agent with Zenrows tools via Composio.",
    model: "openai/gpt-5",
  });

What's happening:

Agent is the core Mastra agent
name is just an identifier for logging and debugging
instructions guide the agent to use tools instead of only answering in natural language
model uses openai("gpt-5") to configure the underlying LLM

Set up interactive chat interface

typescript

let messages: AiMessageType[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end.\n");

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: "> ",
});

rl.prompt();

rl.on("line", async (userInput: string) => {
  const trimmedInput = userInput.trim();

  if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
    console.log("\nGoodbye!");
    rl.close();
    process.exit(0);
  }

  if (!trimmedInput) {
    rl.prompt();
    return;
  }

  messages.push({
    id: crypto.randomUUID(),
    role: "user",
    content: trimmedInput,
  });

  console.log("\nAgent is thinking...\n");

  try {
    const response = await agent.generate(messages, {
      toolsets: {
        zenrows: composioTools,
      },
      maxSteps: 8,
    });

    const { text } = response;

    if (text && text.trim().length > 0) {
      console.log(`Agent: ${text}\n`);
        messages.push({
          id: crypto.randomUUID(),
          role: "assistant",
          content: text,
        });
      }
    } catch (error) {
      console.error("\nError:", error);
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    console.log("\nSession ended.");
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});

What's happening:

messages keeps the full conversation history in Mastra's expected format
agent.generate runs the agent with conversation history and Zenrows toolsets
maxSteps limits how many tool calls the agent can take in a single run
onStepFinish is a hook that prints intermediate steps for debugging

Complete Code

Here's the complete code to get you started with Zenrows and Mastra AI:

typescript

import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({ apiKey: composioAPIKey as string });

async function main() {
  const session = await composio.create(composioUserID as string, {
    toolkits: ["zenrows"],
  });

  const composioMCPUrl = session.mcp.url;

  const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      zenrows: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "zenrows-mastra-agent",
    instructions: "You are an AI agent with Zenrows tools via Composio.",
    model: "openai/gpt-5",
  });

  let messages: AiMessageType[] = [];

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (input: string) => {
    const trimmed = input.trim();
    if (["exit", "quit"].includes(trimmed.toLowerCase())) {
      rl.close();
      return;
    }

    messages.push({ id: crypto.randomUUID(), role: "user", content: trimmed });

    const { text } = await agent.generate(messages, {
      toolsets: { zenrows: composioTools },
      maxSteps: 8,
    });

    if (text) {
      console.log(`Agent: ${text}\n`);
      messages.push({ id: crypto.randomUUID(), role: "assistant", content: text });
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main();

Conclusion

You've built a Mastra AI agent that can interact with Zenrows through Composio's Tool Router. You can extend this further by:

Adding other toolkits like Gmail, Slack, or GitHub
Building a web-based chat interface around this agent
Using multiple MCP endpoints to enable cross-app workflows

TOOLS

Supported Tools

Every Zenrows action and event your agent gets out of the box.

Get ZenRows API Usage Statistics

This tool retrieves the current API usage statistics and limits for your ZenRows account.

Get Detailed Concurrency Status

Requests a test URL through ZenRows and reports concurrency limit and remaining values from response headers when available.

Get Original Status Code

This tool retrieves the original HTTP status code returned by the target website, which is useful for debugging purposes.

Get PDF from URL

This tool generates a PDF version of the scraped content from a given URL.

Get Plaintext Response

This tool extracts plain text content from a webpage using the ZenRows API.

Get Real Estate Property Data

Request structured property data for a Zillow or Idealista property by its platform ID.

Get Response with Compression

Requests a URL through ZenRows and reports response headers and compression-related metadata, including the reported encoding and content length.

Get response headers

A tool to retrieve and parse response headers from ZenRows API requests.

Get Session ID

This tool implements ZenRows' session management functionality to maintain the same IP address across multiple requests for up to 10 minutes.

Get Walmart Product Details

Requests structured Walmart product data from a product URL using ZenRows autoparse and a premium proxy.

Scrape url autoparse

The ZENROWS_SCRAPE_URL_AUTOPARSE tool automatically parses and extracts structured data from any given URL using intelligent parsing capabilities.

Scrape URL HTML

This tool extracts raw HTML data from a given URL using ZenRows' Universal Scraper API.

Scrape URL with CSS Selectors

Scrapes specific webpage elements using a URL and a map of CSS selectors.

Screenshot URL

A tool to capture screenshots of web pages using ZenRows API.

FRAMEWORKS

How to build Zenrows MCP Agent with another framework

Claude Code

Use Zenrows MCP with Claude Code

Claude Cowork

Use Zenrows MCP with Claude Cowork

OpenAI Agents SDK

Use Zenrows MCP with OpenAI Agents SDK

Claude Agent SDK

Use Zenrows MCP with Claude Agent SDK

Muse

Use Zenrows MCP with Muse

Codex

Use Zenrows MCP with Codex

OpenClaw

Use Zenrows MCP with OpenClaw

Hermes

Use Zenrows MCP with Hermes

Atomic Agent

Use Zenrows MCP with Atomic Agent

Google ADK

Use Zenrows MCP with Google ADK

LangChain

Use Zenrows MCP with LangChain

Vercel AI SDK

Use Zenrows MCP with Vercel AI SDK

LlamaIndex

Use Zenrows MCP with LlamaIndex

CrewAI

Use Zenrows MCP with CrewAI

Pydantic AI

Use Zenrows MCP with Pydantic AI

AutoGen

Use Zenrows MCP with AutoGen

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Explore Other Toolkits

Toolkit marketplace

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GitHub

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GitHub is a code hosting platform for version control and collaborative software development. It streamlines project management, code review, and team workflows in one place.

1password

Api Key

1Password is a password manager and digital vault for storing logins, secrets, notes, and secure documents. It helps individuals and teams protect credentials, share access safely, and reduce password risk.

FAQ

Frequently asked questions

With a standalone Zenrows MCP server, the agents and LLMs can only access a fixed set of Zenrows tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Zenrows and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. Mastra AI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Zenrows tools.

Yes, absolutely. You can configure which Zenrows scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Zenrows data and credentials are handled as safely as possible.

Start with Zenrows.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Zenrows tool your agent needs.Free to start.

Start building

How to integrate Zenrows MCP with Mastra AI

Connect Zenrows without auth hassles

Introduction

Also integrate Zenrows with

TL;DR

What is Mastra AI?

What is the Zenrows MCP server, and what's possible with it?

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import libraries and validate environment

Create a Tool Router session for Zenrows

Configure Mastra MCP client and fetch tools

Create the Mastra agent

Set up interactive chat interface

Complete Code

Conclusion

Supported Tools

How to build Zenrows MCP Agent with another framework

Claude Code

Claude Cowork

OpenAI Agents SDK

Claude Agent SDK

Muse

Codex

OpenClaw

Hermes

Atomic Agent

Google ADK

LangChain

Vercel AI SDK

LlamaIndex

CrewAI

Pydantic AI

AutoGen

Explore Other Toolkits

Supabase

Codeinterpreter

GitHub

1password

Frequently asked questions

What are the differences in Tool Router MCP and Zenrows MCP?+

Can I use Tool Router MCP with Mastra AI?+

Can I manage the permissions and scopes for Zenrows while using Tool Router?+

How safe is my data with Composio Tool Router?+

Start with Zenrows.It takes 30 seconds.