How to integrate Zenrows MCP with OpenAI Agents SDK

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Introduction

This guide walks you through connecting Zenrows to the OpenAI Agents SDK 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, show my current zenrows api usage stats through natural language commands.

This guide will help you understand how to give your OpenAI Agents SDK 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.

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the necessary dependencies
  • Initialize Composio and create a Tool Router session for Zenrows
  • Configure an AI agent that can use Zenrows as a tool
  • Run a live chat session where you can ask the agent to perform Zenrows operations

What is open-ai-agents-sdk?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.

Key features include:

  • Hosted MCP Tools: Connect to external services through hosted MCP endpoints
  • SQLite Sessions: Persist conversation history across interactions
  • Simple API: Clean interface with Agent, Runner, and tool configuration
  • Streaming Support: Real-time response streaming for interactive applications

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.

Supported Tools & Triggers

Tools
Get ZenRows API Usage StatisticsThis tool retrieves the current api usage statistics and limits for your zenrows account.
Get Concurrency StatusThis tool retrieves the current concurrency status of your zenrows api usage.
Get Detailed Concurrency StatusThis 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 CodeThis tool retrieves the original http status code returned by the target website, which is useful for debugging purposes.
Get PDF from URLThis tool generates a pdf version of the scraped content from a given url.
Get Plaintext ResponseThis tool extracts plain text content from a webpage using the zenrows api.
Get Real Estate Property DataA specialized tool for extracting structured data from real estate platforms like zillow and idealista.
Get Response with CompressionA tool to fetch content from a url using the zenrows api with compression enabled to optimize bandwidth usage and improve performance.
Get response headersA tool to retrieve and parse response headers from zenrows api requests.
Get Session IDThis tool implements zenrows' session management functionality to maintain the same ip address across multiple requests for up to 10 minutes.
Get Walmart Product DetailsThis tool allows users to extract detailed product information from walmart using zenrows' specialized e-commerce scraping api.
Scrape urlScrape and extract data from a specified url.
Scrape url autoparseThe zenrows scrape url autoparse tool automatically parses and extracts structured data from any given url using intelligent parsing capabilities.
Scrape URL HTMLThis tool extracts raw html data from a given url using zenrows' universal scraper api.
Scrape URL with CSS SelectorsThis tool allows users to scrape specific elements from a webpage using css selectors.
Screenshot URLA tool to capture screenshots of web pages using zenrows api.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router follows a three-phase workflow:

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

Step-by-step Guide

Prerequisites

Before starting, make sure you have:
  • Composio API Key and OpenAI API Key
  • Primary know-how of OpenAI Agents SDK
  • A live Zenrows project
  • Some knowledge of Python or Typescript

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key

Install dependencies

pip install composio_openai_agents openai-agents python-dotenv

Install the Composio SDK and the OpenAI Agents SDK.

Set up environment variables

bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com

Create a .env file and add your OpenAI and Composio API keys.

Import dependencies

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
What's happening:
  • You're importing all necessary libraries.
  • The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Zenrows.

Set up the Composio instance

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
What's happening:
  • load_dotenv() loads your .env file so OPENAI_API_KEY and COMPOSIO_API_KEY are available as environment variables.
  • Creating a Composio instance using the API Key and OpenAIAgentsProvider class.

Create a Tool Router session

# Create a Zenrows Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["zenrows"]
)

mcp_url = session.mcp.url

What is happening:

  • You give the Tool Router the user id and the toolkits you want available. Here, it is only zenrows.
  • The router checks the user's Zenrows connection and prepares the MCP endpoint.
  • The returned session.mcp.url is the MCP URL that your agent will use to access Zenrows.
  • This approach keeps things lightweight and lets the agent request Zenrows tools only when needed during the conversation.

Configure the agent

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Zenrows. "
        "Help users perform Zenrows operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
What's happening:
  • We're creating an Agent instance with a name, model (gpt-5), and clear instructions about its purpose.
  • The agent's instructions tell it that it can access Zenrows and help with queries, inserts, updates, authentication, and fetching database information.
  • The tools array includes a HostedMCPTool that connects to the MCP server URL we created earlier.
  • The headers dict includes the Composio API key for secure authentication with the MCP server.
  • require_approval: 'never' means the agent can execute Zenrows operations without asking for permission each time, making interactions smoother.

Start chat loop and handle conversation

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
What's happening:
  • The program prints a session URL that you visit to authorize Zenrows.
  • After authorization, the chat begins.
  • Each message you type is processed by the agent using Runner.run().
  • The responses are printed to the console, and conversations are saved locally using SQLite.
  • Typing exit, quit, or q cleanly ends the chat.

Complete Code

Here's the complete code to get you started with Zenrows and open-ai-agents-sdk:

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["zenrows"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Zenrows. "
        "Help users perform Zenrows operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())

Conclusion

This was a starter code for integrating Zenrows MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Zenrows.

Key features:

  • Hosted MCP tool integration through Composio's Tool Router
  • SQLite session persistence for conversation history
  • Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

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FAQ

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

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.

Can I use Tool Router MCP with OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK 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.

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

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.

How safe is my data with Composio Tool Router?

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.

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