How to integrate Zerobounce MCP with CrewAI

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Introduction

This guide walks you through connecting Zerobounce to CrewAI using the Composio tool router. By the end, you'll have a working Zerobounce agent that can validate a list of 100 new signups, score this email for lead quality, check deliverability of mark@gmail.com, get activity data for a specific email through natural language commands.

This guide will help you understand how to give your CrewAI agent real control over a Zerobounce account through Composio's Zerobounce 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 Zerobounce connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Zerobounce
  • Build a conversational loop where your agent can execute Zerobounce 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 Zerobounce MCP server, and what's possible with it?

The Zerobounce MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Zerobounce account. It provides structured and secure access to your email validation and deliverability tools, so your agent can perform actions like validating emails, scoring leads, managing bulk jobs, and analyzing engagement—all automatically.

  • Real-time email validation: Instantly check if email addresses are valid, risky, or undeliverable before sending campaigns or updating your lists.
  • Bulk validation and processing: Upload files and process hundreds of emails or domains at once, with tools for tracking job status and retrieving results.
  • AI-powered lead scoring: Score individual email addresses using Zerobounce AI to assess lead quality and prioritize outreach.
  • Domain and pattern analysis: Identify common email address formats for any domain or run bulk domain searches to optimize your contact strategies.
  • Allowlist and blocklist management: Easily update your allow and block lists to fine-tune which addresses pass or fail validation, keeping your lists clean and secure.

Supported Tools & Triggers

Tools
Get Activity DataTool to get activity data (opens, clicks, etc.
AI Scoring Single EmailTool to score a single email address using zerobounce ai.
Allow or Block ListTool to manage allowlist and blocklist for email validation.
Batch Validate EmailsTool to validate a batch of email addresses in real time.
Delete fileTool to delete a file that was submitted for bulk validation.
Domain Search File StatusTool to get the processing status of a file submitted for bulk domain search.
Domain Search Get FileTool to download the results file for a completed bulk domain search job.
Domain Search Send FileTool to upload a file for bulk domain search.
Domain Search SingleTool to identify common email address formats for a given domain.
Delete Email Finder FileTool to delete a file that was submitted for bulk email finding.
Email Finder File StatusTool to get the processing status of a file submitted for bulk email finding.
Email Finder Send FileTool to upload a file for bulk email finding.
Email Finder SingleTool to find an email address for a given person and domain.
Get API UsageTool to retrieve api usage statistics for a given period.
Get Credit BalanceTool to retrieve your current zerobounce email validation credit balance.
List EvaluatorTool to evaluate the quality of an email list.
Send FileTool to upload a file for bulk email validation.
Validate EmailTool to validate a single email address in real time.

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 Zerobounce 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 Zerobounce 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 Zerobounce MCP URL

Create a Composio Tool Router session for Zerobounce

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["zerobounce"],
)
url = session.mcp.url
What's happening:
  • You create a Zerobounce only session through Composio
  • Composio returns an MCP HTTP URL that exposes Zerobounce 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="Zerobounce Assistant",
    goal="Help users interact with Zerobounce through natural language commands",
    backstory=(
        "You are an expert assistant with access to Zerobounce tools. "
        "You can perform various Zerobounce 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 Zerobounce 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 Zerobounce 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 Zerobounce 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_zerobounce_agent.py

Complete Code

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

python
# file: crewai_zerobounce_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 Zerobounce session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["zerobounce"],
    )
    url = session.mcp.url

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

    # Create Zerobounce assistant agent
    toolkit_agent = Agent(
        role="Zerobounce Assistant",
        goal="Help users interact with Zerobounce through natural language commands",
        backstory=(
            "You are an expert assistant with access to Zerobounce tools. "
            "You can perform various Zerobounce 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 Zerobounce 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 Zerobounce 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 Zerobounce through Composio's Tool Router. The agent can perform Zerobounce 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 Zerobounce MCP Agent with another framework

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LangChain

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Vercel AI SDK

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Mastra AI

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LlamaIndex

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Use Zerobounce MCP with LlamaIndex

FAQ

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

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

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

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

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