How to integrate Clearout MCP with Autogen

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Introduction

This guide walks you through connecting Clearout to AutoGen using the Composio tool router. By the end, you'll have a working Clearout agent that can validate a list of emails for deliverability, check if this email is a business account, find the most likely domain for acme corp, download results from my last bulk email finder job through natural language commands.

This guide will help you understand how to give your AutoGen agent real control over a Clearout account through Composio's Clearout 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 Clearout
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Clearout tools
  • Run a live chat loop where you ask the agent to perform Clearout 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 Clearout MCP server, and what's possible with it?

The Clearout MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Clearout account. It provides structured and secure access to email validation, prospecting, and enrichment tools, so your agent can perform actions like verifying email addresses, finding business contacts, checking domain details, and bulk-processing lists on your behalf.

  • AI-powered email verification: Instantly validate single or bulk email addresses to ensure deliverability and reduce bounce rates.
  • Email prospecting and enrichment: Find emails for people or companies, complete missing contact data, and verify if accounts are business or personal.
  • Domain and company intelligence: Retrieve company domains from names, fetch MX records, or pull WHOIS information to understand your leads and their infrastructure.
  • Disposable and catch-all detection: Check if an email is temporary or if a domain accepts all mail, helping you maintain list quality and avoid spam traps.
  • Bulk job automation: Upload, process, monitor, cancel, and download results for large-scale email finding and verification tasks, all through your agent.

Supported Tools & Triggers

Tools
Autocomplete Company to DomainTool to autocomplete company names to probable domains with confidence scores.
Business Account VerifyTool to check if an email belongs to a business/work account.
Catch-All VerifyTool to check if an email domain is catch-all.
Verify Disposable EmailTool to check if an email is from a disposable provider.
Find Domain MX RecordsTool to retrieve MX records for a domain in priority order.
Fetch Domain WHOIS InformationTool to fetch WHOIS record for a domain.
Bulk Email FinderTool to upload a CSV or XLSX contacts file for bulk email finding.
Cancel Bulk Email Finder JobTool to cancel a running bulk email finder job.
Bulk Email Finder Result DownloadTool to generate a bulk email finder result download URL.
Bulk Email VerifyTool to upload a CSV or XLSX file for bulk email verification.
Cancel Bulk Email Verification JobTool to cancel a running bulk email verification job.
Bulk Email Verify Progress StatusTool to retrieve progress for a bulk email verification job.
Bulk Email Verify Result DownloadTool to obtain a temporary URL for bulk email verification results.
Email Verify Get CreditsTool to fetch available email verification credits.
Instant Email VerifierTool to instantly verify a single email address.
Verify Free Email AccountTool to detect if an email is from a free email service provider.
Verify Gibberish EmailTool to verify if an email address is gibberish.
Reverse Lookup Company by DomainTool to find company information by domain.
Reverse Lookup Person by EmailTool to retrieve a person’s profile from an email address.
Find Person via LinkedIn URLTool to discover person information via a LinkedIn profile URL.
Role Account VerifierTool to determine if an email is a role-based account.

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 Clearout 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 Clearout 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 Clearout 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 Clearout session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["clearout"]
    )
    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 Clearout 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 Clearout assistant agent with MCP tools
    agent = AssistantAgent(
        name="clearout_assistant",
        description="An AI assistant that helps with Clearout 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 Clearout 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 Clearout 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 Clearout 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 Clearout 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 Clearout session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["clearout"]
    )
    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 Clearout assistant agent with MCP tools
        agent = AssistantAgent(
            name="clearout_assistant",
            description="An AI assistant that helps with Clearout 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 Clearout 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 Clearout 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 Clearout, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

How to build Clearout MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Clearout MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Clearout MCP with Claude Agent SDK

Google ADK

Google ADK

Use Clearout MCP with Google ADK

LangChain

LangChain

Use Clearout MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Clearout MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Clearout MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Clearout MCP with LlamaIndex

CrewAI

CrewAI

Use Clearout MCP with CrewAI

FAQ

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

With a standalone Clearout MCP server, the agents and LLMs can only access a fixed set of Clearout tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Clearout 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 Clearout tools.

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

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

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