How to integrate Clearout MCP with CrewAI

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Introduction

This guide walks you through connecting Clearout to CrewAI 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 CrewAI 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 a Composio API key and configure your Clearout connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Clearout
  • Build a conversational loop where your agent can execute Clearout operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

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

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Clearout connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python

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 crewai crewai-tools python-dotenv
What's happening:
  • composio connects your agent to Clearout via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools includes MCP helpers
  • python-dotenv loads environment variables from .env

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
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 with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model

Import dependencies

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional import if you plan to adapt tools
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Clearout MCP URL

Create a Composio Tool Router session for Clearout

python
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:
  • You create a Clearout only session through Composio
  • Composio returns an MCP HTTP URL that exposes Clearout tools

Configure the LLM

python
llm = LLM(
    model="gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
)
What's happening:
  • CrewAI will call this LLM for planning and responses
  • You can swap in a different model if needed

Attach the MCP server and create the agent

python
toolkit_agent = Agent(
    role="Clearout Assistant",
    goal="Help users interact with Clearout through natural language commands",
    backstory=(
        "You are an expert assistant with access to Clearout tools. "
        "You can perform various Clearout operations on behalf of the user."
    ),
    mcps=[
        MCPServerHTTP(
            url=url,
            streamable=True,
            cache_tools_list=True,
            headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
        ),
    ],
    llm=llm,
    verbose=True,
    max_iter=10,
)
What's happening:
  • MCPServerHTTP connects the agent to the Clearout MCP endpoint
  • cache_tools_list saves a tools catalog for faster subsequent runs
  • verbose helps you see what the agent is doing

Add a REPL loop with Task and Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to perform Clearout operations.\n")

conversation_context = ""

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

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

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Based on the conversation history:\n{conversation_context}\n\n"
            f"Current user request: {user_input}\n\n"
            f"Please help the user with their Clearout related request."
        ),
        expected_output="A helpful response addressing the user's request",
        agent=toolkit_agent,
    )

    crew = Crew(
        agents=[toolkit_agent],
        tasks=[task],
        verbose=False,
    )

    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's happening:
  • You build a simple chat loop and keep a running context
  • Each user turn becomes a Task handled by the same agent
  • Crew executes the task and returns a response

Run the application

python
if __name__ == "__main__":
    main()
What's happening:
  • Standard Python entry point so you can run python crewai_clearout_agent.py

Complete Code

Here's the complete code to get you started with Clearout and CrewAI:

python
# file: crewai_clearout_agent.py
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()

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 LLM
    llm = LLM(
        model="gpt-5-mini",
        api_key=os.getenv("OPENAI_API_KEY"),
    )

    # Create Clearout assistant agent
    toolkit_agent = Agent(
        role="Clearout Assistant",
        goal="Help users interact with Clearout through natural language commands",
        backstory=(
            "You are an expert assistant with access to Clearout tools. "
            "You can perform various Clearout operations on behalf of the user."
        ),
        mcps=[
            MCPServerHTTP(
                url=url,
                streamable=True,
                cache_tools_list=True,
                headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
            ),
        ],
        llm=llm,
        verbose=True,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Try asking the agent to perform Clearout operations.\n")

    conversation_context = ""

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

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

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Based on the conversation history:\n{conversation_context}\n\n"
                f"Current user request: {user_input}\n\n"
                f"Please help the user with their Clearout related request."
            ),
            expected_output="A helpful response addressing the user's request",
            agent=toolkit_agent,
        )

        crew = Crew(
            agents=[toolkit_agent],
            tasks=[task],
            verbose=False,
        )

        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

if __name__ == "__main__":
    main()

Conclusion

You now have a CrewAI agent connected to Clearout through Composio's Tool Router. The agent can perform Clearout operations through natural language commands. Next steps:
  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations

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

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 CrewAI?

Yes, you can. CrewAI 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.

Used by agents from

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Context
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Rolai

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