# How to integrate Zenrows MCP with OpenAI Agents SDK

```json
{
  "title": "How to integrate Zenrows MCP with OpenAI Agents SDK",
  "toolkit": "Zenrows",
  "toolkit_slug": "zenrows",
  "framework": "OpenAI Agents SDK",
  "framework_slug": "open-ai-agents-sdk",
  "url": "https://composio.dev/toolkits/zenrows/framework/open-ai-agents-sdk",
  "markdown_url": "https://composio.dev/toolkits/zenrows/framework/open-ai-agents-sdk.md",
  "updated_at": "2026-05-06T08:34:34.376Z"
}
```

## 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 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.

## Also integrate Zenrows with

- [Claude Agent SDK](https://composio.dev/toolkits/zenrows/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/zenrows/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/zenrows/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/zenrows/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/zenrows/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/zenrows/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/zenrows/framework/cli)
- [Google ADK](https://composio.dev/toolkits/zenrows/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/zenrows/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/zenrows/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/zenrows/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/zenrows/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/zenrows/framework/crew-ai)

## 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 OpenAI 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

| Tool slug | Name | Description |
|---|---|---|
| `ZENROWS_GET_API_USAGE` | Get ZenRows API Usage Statistics | This tool retrieves the current api usage statistics and limits for your zenrows account. it is an independent action that requires no additional parameters besides authentication. it is useful for monitoring api usage and ensuring that the account has sufficient remaining credits. |
| `ZENROWS_GET_CONCURRENCY_STATUS` | Get Concurrency Status | This tool retrieves the current concurrency status of your zenrows api usage. it reports the maximum number of concurrent requests allowed by your plan and the number of available concurrent request slots. it is useful for monitoring api usage, implementing rate limiting, debugging request issues, and capacity planning. |
| `ZENROWS_GET_CONCURRENCY_STATUS_DETAILED` | 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. it is essential for monitoring real-time api usage, managing concurrent requests, and ensuring optimal performance within plan limits. |
| `ZENROWS_GET_ORIGINAL_STATUS` | Get Original Status Code | This tool retrieves the original http status code returned by the target website, which is useful for debugging purposes. it returns the original status code in the response headers under 'x-zenrows-original-status'. it can also provide the full response body and error messages, helping with debugging scraping issues, verifying website responses, monitoring availability, and understanding website behavior. |
| `ZENROWS_GET_PDF_FROM_URL` | Get PDF from URL | This tool generates a pdf version of the scraped content from a given url. it requires javascript rendering to be enabled and sets the response type to pdf, making it ideal for archiving web pages, creating documentation, generating reports, or saving articles for offline reading. |
| `ZENROWS_GET_PLAINTEXT` | Get Plaintext Response | This tool extracts plain text content from a webpage using the zenrows api. by using the response type=plaintext parameter, it strips html tags and formats the content into clean, plain text. it's useful for extracting readable content for nlp, summarization, or archiving purposes. |
| `ZENROWS_GET_REAL_ESTATE_DATA` | Get Real Estate Property Data | A specialized tool for extracting structured data from real estate platforms like zillow and idealista. it leverages zenrows' real estate api to fetch comprehensive property information, including property details, location information, features, seller details, and more, in a structured format. |
| `ZENROWS_GET_RESPONSE_COMPRESSION` | 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. it supports gzip, deflate, and br compression encodings, handles decompression automatically, and provides compression statistics along with the decompressed content. |
| `ZENROWS_GET_RESPONSE_HEADERS` | Get response headers | A tool to retrieve and parse response headers from zenrows api requests. it provides critical metadata such as concurrency limits, available request slots, request cost, unique request id, and final url after redirects, which is essential for monitoring usage, debugging, and optimizing requests. |
| `ZENROWS_GET_SESSION_ID` | Get Session ID | This tool implements zenrows' session management functionality to maintain the same ip address across multiple requests for up to 10 minutes. it supports parameters like url, session id, and premium proxy, and is useful for maintaining consistent scraping sessions, simulating real user behavior, and avoiding detection by anti-bot systems. |
| `ZENROWS_GET_WALMART_PRODUCT` | Get Walmart Product Details | This tool allows users to extract detailed product information from walmart using zenrows' specialized e-commerce scraping api. it provides structured data for walmart products including product details, pricing, availability, and more. |
| `ZENROWS_SCRAPE_URL` | Scrape url | Scrape and extract data from a specified url. this action allows you to collect and process web data effortlessly using the zenrows api. |
| `ZENROWS_SCRAPE_URL_AUTOPARSE` | Scrape url autoparse | The zenrows scrape url autoparse tool automatically parses and extracts structured data from any given url using intelligent parsing capabilities. it eliminates the need for manual css selectors by auto-identifying relevant content on web pages, returning data such as titles, main content, meta descriptions, images, links, prices, and contact information in a structured json format. |
| `ZENROWS_SCRAPE_URL_HTML` | Scrape URL HTML | This tool extracts raw html data from a given url using zenrows' universal scraper api. it focuses on retrieving the pure html content of the webpage without automatic parsing or data extraction. it supports parameters such as js render for enabling javascript rendering, custom headers for custom http headers, premium proxy for using premium proxies, and session id for maintaining the same ip across multiple requests. |
| `ZENROWS_SCRAPE_WITH_CSS_SELECTORS` | Scrape URL with CSS Selectors | This tool allows users to scrape specific elements from a webpage using css selectors. it is particularly useful for targeted data extraction rather than retrieving the entire page content. the endpoint takes a url and a json object containing css selectors for parsing elements such as titles, links, images, and prices, and includes optional parameters like using premium proxies, specifying response wait times, and custom headers among others. |
| `ZENROWS_SCREENSHOT_URL` | Screenshot URL | A tool to capture screenshots of web pages using zenrows api. this tool allows you to take screenshots of entire web pages or specific elements, with customizable options for format and quality. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Zenrows MCP server is an implementation of the Model Context Protocol that connects your AI agent to Zenrows. It provides structured and secure access so your agent can perform Zenrows operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. 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

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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
- Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Go to Settings and copy your API key.

### 2. Install dependencies

Install the Composio SDK and the OpenAI Agents SDK.
```python
pip install composio_openai_agents openai-agents python-dotenv
```

```typescript
npm install @composio/openai-agents @openai/agents dotenv
```

### 3. Set up environment variables

Create a .env file and add your OpenAI and Composio API keys.
```bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com
```

### 4. Import dependencies

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.
```python
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
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';
```

### 5. Set up the Composio instance

No description provided.
```python
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())
```

```typescript
dotenv.config();

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.USER_ID;

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});
```

### 6. Create a Tool Router session

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

mcp_url = session.mcp.url
```

```typescript
// Create Tool Router session for Zenrows
const session = await composio.create(userId as string, {
  toolkits: ['zenrows'],
});
const mcpUrl = session.mcp.url;
```

### 7. Configure the agent

No description provided.
```python
# 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",
            }
        )
    ],
)
```

```typescript
// Configure agent with MCP tool
const agent = new 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({
      serverLabel: 'tool_router',
      serverUrl: mcpUrl,
      headers: { 'x-api-key': composioApiKey },
      requireApproval: 'never',
    }),
  ],
});
```

### 8. Start chat loop and handle conversation

No description provided.
```python
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())
```

```typescript
// Keep conversation state across turns
const conversationSession = new OpenAIConversationsSession();

// Simple CLI
const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: 'You: ',
});

console.log('\nComposio Tool Router session created.');
console.log('\nChat started. Type your requests below.');
console.log("Commands: 'exit', 'quit', or 'q' to end\n");

try {
  const first = await run(agent, 'What can you help me with?', { session: conversationSession });
  console.log(`Assistant: ${first.finalOutput}\n`);
} catch (e) {
  console.error('Error:', e instanceof Error ? e.message : e, '\n');
}

rl.prompt();

rl.on('line', async (userInput) => {
  const text = userInput.trim();

  if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
    console.log('Goodbye!');
    rl.close();
    process.exit(0);
  }

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

  try {
    const result = await run(agent, text, { session: conversationSession });
    console.log(`\nAssistant: ${result.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();
});

rl.on('close', () => {
  console.log('\n👋 Session ended.');
  process.exit(0);
});
```

## Complete Code

```python
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())
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.USER_ID;

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});

async function main() {
  // Create Tool Router session
  const session = await composio.create(userId as string, {
    toolkits: ['zenrows'],
  });
  const mcpUrl = session.mcp.url;

  // Configure agent with MCP tool
  const agent = new 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({
        serverLabel: 'tool_router',
        serverUrl: mcpUrl,
        headers: { 'x-api-key': composioApiKey },
        requireApproval: 'never',
      }),
    ],
  });

  // Keep conversation state across turns
  const conversationSession = new OpenAIConversationsSession();

  // Simple CLI
  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: ',
  });

  console.log('\nComposio Tool Router session created.');
  console.log('\nChat started. Type your requests below.');
  console.log("Commands: 'exit', 'quit', or 'q' to end\n");

  try {
    const first = await run(agent, 'What can you help me with?', { session: conversationSession });
    console.log(`Assistant: ${first.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();

  rl.on('line', async (userInput) => {
    const text = userInput.trim();

    if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
      console.log('Goodbye!');
      rl.close();
      process.exit(0);
    }

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

    try {
      const result = await run(agent, text, { session: conversationSession });
      console.log(`\nAssistant: ${result.finalOutput}\n`);
    } catch (e) {
      console.error('Error:', e instanceof Error ? e.message : e, '\n');
    }

    rl.prompt();
  });

  rl.on('close', () => {
    console.log('\nSession ended.');
    process.exit(0);
  });
}

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

## 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.

## How to build Zenrows MCP Agent with another framework

- [Claude Agent SDK](https://composio.dev/toolkits/zenrows/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/zenrows/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/zenrows/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/zenrows/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/zenrows/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/zenrows/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/zenrows/framework/cli)
- [Google ADK](https://composio.dev/toolkits/zenrows/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/zenrows/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/zenrows/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/zenrows/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/zenrows/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/zenrows/framework/crew-ai)

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- [GitHub](https://composio.dev/toolkits/github) - 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.
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- [Anchor browser](https://composio.dev/toolkits/anchor_browser) - Anchor browser is a developer platform for AI-powered web automation. It transforms complex browser actions into easy API endpoints for streamlined web interaction.
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- [Apiverve](https://composio.dev/toolkits/apiverve) - Apiverve delivers a suite of powerful APIs that simplify integration for developers. It's designed for reliability and scalability so you can build faster, smarter applications without the integration headache.
- [Appcircle](https://composio.dev/toolkits/appcircle) - Appcircle is an enterprise-grade mobile CI/CD platform for building, testing, and publishing mobile apps. It streamlines mobile DevOps so teams ship faster and with more confidence.
- [Appdrag](https://composio.dev/toolkits/appdrag) - Appdrag is a cloud platform for building websites, APIs, and databases with drag-and-drop tools and code editing. It accelerates development and iteration by combining hosting, database management, and low-code features in one place.
- [Appveyor](https://composio.dev/toolkits/appveyor) - AppVeyor is a cloud-based continuous integration service for building, testing, and deploying applications. It helps developers automate and streamline their software delivery pipelines.
- [Backendless](https://composio.dev/toolkits/backendless) - Backendless is a backend-as-a-service platform for mobile and web apps, offering database, file storage, user authentication, and APIs. It helps developers ship scalable applications faster without managing server infrastructure.
- [Baserow](https://composio.dev/toolkits/baserow) - Baserow is an open-source no-code database platform for building collaborative data apps. It makes it easy for teams to organize data and automate workflows without writing code.
- [Bench](https://composio.dev/toolkits/bench) - Bench is a benchmarking tool for automated performance measurement and analysis. It helps you quickly evaluate, compare, and track your systems or workflows.
- [Better stack](https://composio.dev/toolkits/better_stack) - Better Stack is a monitoring, logging, and incident management solution for apps and services. It helps teams ensure application reliability and performance with real-time insights.
- [Bitbucket](https://composio.dev/toolkits/bitbucket) - Bitbucket is a Git-based code hosting and collaboration platform for teams. It enables secure repository management and streamlined code reviews.
- [Blazemeter](https://composio.dev/toolkits/blazemeter) - Blazemeter is a continuous testing platform for web and mobile app performance. It empowers teams to automate and analyze large-scale tests with ease.
- [Blocknative](https://composio.dev/toolkits/blocknative) - Blocknative delivers real-time mempool monitoring and transaction management for public blockchains. Instantly track pending transactions and optimize blockchain interactions with live data.

## Frequently Asked Questions

### 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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