How to integrate Scrape do MCP with Autogen

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Introduction

This guide walks you through connecting Scrape do to AutoGen using the Composio tool router. By the end, you'll have a working Scrape do agent that can scrape product prices from a dynamic website, extract news headlines with javascript rendering, bypass cloudflare to get full page html, scrape mobile version of a web page through natural language commands.

This guide will help you understand how to give your AutoGen agent real control over a Scrape do account through Composio's Scrape do 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 required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Scrape do
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Scrape do tools
  • Run a live chat loop where you ask the agent to perform Scrape do operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

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

The Scrape do MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Scrape do account. It provides structured and secure access to robust web scraping tools, so your agent can perform actions like scraping dynamic pages, managing sessions, setting custom headers or proxies, and extracting structured data from any website on your behalf.

  • Dynamic page scraping with headless browsers: Retrieve fully rendered HTML content from JavaScript-heavy or protected websites by leveraging advanced browser emulation and proxy rotation.
  • Custom scraping session management: Set device type, cookies, wait times, and custom headers to imitate different users, maintain sessions, or access device-specific content for tailored data extraction.
  • Proxy and anti-bot bypass control: Enable super or proxy modes to utilize residential, mobile, or datacenter proxies, helping your agent bypass strict anti-bot systems and geo-restrictions seamlessly.
  • Targeted resource filtering: Block specific URLs like ads or analytics scripts during scraping to increase speed, avoid distractions, and improve privacy.
  • Account usage and statistics retrieval: Access real-time usage stats, subscription status, and remaining request limits so your agent can monitor scraping quotas and avoid interruptions.

Supported Tools & Triggers

Tools
Get Account InformationRetrieves account information and usage statistics from scrape.
Get rendered page contentThis tool allows you to scrape web pages with javascript rendering enabled.
Scrape webpage using scrape.doA tool to scrape web pages using scrape.
Use Scrape.do Proxy ModeThis tool implements the proxy mode functionality of scrape.
Set Cookies for ScrapingThis tool allows users to set specific cookies for their scraping requests to a target website.
Set Scrape.do Super ModeThe scrape do set super mode tool enables enhanced scraping by using residential and mobile proxies, bypassing blocks and restrictions associated with datacenter ips.
Block specific URLs during scrapingThis tool allows users to block specific urls during the scraping process.
Set custom headers for scrape.do requestA tool to send custom headers with scrape.
Set Custom Wait TimeThis tool sets the custom wait time in milliseconds after page load when using the render option in scrape.
Set Device Type for ScrapingThis tool allows users to set the device type (desktop, mobile, or tablet) for making scraping requests.
Set Disable RedirectionControls the automatic redirection behavior of scrape.
Set Pure Cookies ModeThis tool enables getting the original set-cookie headers from target websites instead of the processed scrape.
Set Regional Geolocation for ScrapingThis tool allows users to set a broader geographical targeting by specifying a region code instead of a specific country code.
Set Retry TimeoutThis tool allows users to set the maximum wait time (in milliseconds) before retrying a failed request in scrape.
Set Screenshot Capture for ScrapingThis tool enables the screenshot functionality for the scrape.
Set Session ID for Sticky SessionsThis tool implements the session id functionality for scrape.
Set Wait For SelectorThis action allows setting a css selector to wait for before considering the page load complete.
Set Wait Until ConditionThis tool sets the waituntil parameter for the scrape.
Monitor WebSocket requests using scrape.doThis tool provides the ability to view websocket requests made by a webpage.

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

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Scrape do account you can connect to Composio
  • Some basic familiarity with Autogen and Python async

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
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Scrape do via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Scrape do connections to use

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Scrape do session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["scrape_do"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Scrape do tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Scrape do assistant agent with MCP tools
    agent = AssistantAgent(
        name="scrape_do_assistant",
        description="An AI assistant that helps with Scrape do operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Scrape do tools from the workbench

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Scrape do related question or task to the agent.\n")

# Conversation loop
while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Scrape do tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Scrape do and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Scrape do session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["scrape_do"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Scrape do assistant agent with MCP tools
        agent = AssistantAgent(
            name="scrape_do_assistant",
            description="An AI assistant that helps with Scrape do operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Scrape do related question or task to the agent.\n")

        # Conversation loop
        while True:
            user_input = input("You: ").strip()

            if user_input.lower() in ['exit', 'quit', 'bye']:
                print("\nGoodbye!")
                break

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Scrape do through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Scrape do, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

How to build Scrape do MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Scrape do MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Scrape do MCP with Claude Agent SDK

Google ADK

Google ADK

Use Scrape do MCP with Google ADK

LangChain

LangChain

Use Scrape do MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Scrape do MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Scrape do MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Scrape do MCP with LlamaIndex

CrewAI

CrewAI

Use Scrape do MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Scrape do MCP?

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

Can I use Tool Router MCP with Autogen?

Yes, you can. Autogen 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 Scrape do tools.

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

Yes, absolutely. You can configure which Scrape do 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 Scrape do data and credentials are handled as safely as possible.

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