How to integrate Zenrows MCP with Mastra AI

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 download a pdf of this news article, extract plain text from the given webpage, get latest property data from zillow 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.

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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.

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Introduction

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

OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenClaw Hermes CLI Google ADK LangChain Vercel AI SDK LlamaIndex CrewAI

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 advanced web scraping capabilities, so your agent can extract structured data, bypass CAPTCHAs, convert pages to PDF, and monitor your API usage on your behalf.

Intelligent web data extraction: Direct your agent to scrape and extract plain text or structured data from dynamic websites, including specialized real estate property data from platforms like Zillow or Idealista.
PDF and content generation: Ask your agent to convert any web page into a PDF or retrieve clean, formatted plain text for archiving, documentation, or offline reading.
Seamless CAPTCHA and block bypassing: Enable your agent to gather data from sites protected by CAPTCHAs or anti-bot systems without manual intervention.
Real-time API usage monitoring: Have the agent check your account’s current API usage, concurrency status, and limits to help manage credits and avoid interruptions.
Session and compression management: Instruct your agent to maintain consistent scraping sessions, handle compression to optimize bandwidth, and retrieve detailed response headers for debugging and performance optimization.

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

This tool provides detailed information about the current concurrency status and limits of your ZenRows account by making a request to the API and analyzing the response headers.

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

A specialized tool for extracting structured data from real estate platforms like Zillow and Idealista.

Get Response with Compression

A tool to fetch content from a URL using the ZenRows API with compression enabled to optimize bandwidth usage and improve performance.

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

This tool allows users to extract detailed product information from Walmart using ZenRows' specialized e-commerce scraping API.

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

This tool allows users to scrape specific elements from a webpage using 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

OpenAI Agents SDK

Use Zenrows MCP with OpenAI Agents SDK

Claude Agent SDK

Use Zenrows MCP with Claude Agent SDK

Claude Code

Use Zenrows MCP with Claude Code

Claude Cowork

Use Zenrows MCP with Claude Cowork

Codex

Use Zenrows MCP with Codex

OpenClaw

Use Zenrows MCP with OpenClaw

Hermes

Use Zenrows MCP with Hermes

CLI

Use Zenrows MCP with CLI

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

MORE TOOLKITS

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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.

Ably

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Ably is a real-time messaging platform for live chat and data sync in modern apps. It offers global scale and rock-solid reliability for seamless, instant experiences.

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

Customer stories

Teams ship real agents on Composio

GET STARTED FOR FREE BOOK A DEMO

Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati

Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya

Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave

CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu

Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola

Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche

Co-Founder, Ingram Technologies

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

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

OpenClaw

Hermes

CLI

Google ADK

LangChain

Vercel AI SDK

LlamaIndex

CrewAI

Explore Other Toolkits

Supabase

Codeinterpreter

GitHub

Ably

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.

Teams ship real agents on Composio