How to integrate Mx toolbox MCP with Autogen

This guide walks you through connecting Mx toolbox to AutoGen using the Composio tool router. By the end, you'll have a working Mx toolbox agent that can check if your domain is blacklisted, get current mx records for example.com, run a ping test on our mail server through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Mx toolbox account through Composio's Mx toolbox MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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MxToolbox is a network diagnostic and monitoring service with advanced DNS, blacklist, and email health tools. It helps IT teams quickly troubleshoot and ensure reliable email and network performance.

13 Tools

Introduction

This guide walks you through connecting Mx toolbox to AutoGen using the Composio tool router. By the end, you'll have a working Mx toolbox agent that can check if your domain is blacklisted, get current mx records for example.com, run a ping test on our mail server through natural language commands.

This guide will help you understand how to give your AutoGen agent real control over a Mx toolbox account through Composio's Mx toolbox MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Mx toolbox with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

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 Mx toolbox
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Mx toolbox tools
  • Run a live chat loop where you ask the agent to perform Mx toolbox 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 Mx toolbox MCP server, and what's possible with it?

The Mx toolbox MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Mx toolbox account. It provides structured and secure access to network diagnostic and email health tools, so your agent can perform actions like DNS lookups, blacklist checks, email authentication analysis, and connectivity testing on your behalf.

  • Automated DNS and MX record lookups: Instantly retrieve DNS, MX, DKIM, DMARC, and MTA-STS records for any domain to verify configuration and troubleshoot email delivery issues.
  • Blacklist monitoring and alerting: Check if your domain or IP is listed on common blacklists, helping you stay ahead of email deliverability problems and security risks.
  • Email authentication validation: Validate BIMI, DKIM, and DMARC records to ensure your domain's outgoing emails are properly authenticated and protected against spoofing.
  • Network and SMTP diagnostics: Run ping, HTTP, and SMTP lookups to diagnose connectivity issues, measure latency, or assess mail server responsiveness—no manual testing required.
  • Brand and security checks: Use BIMI and MTA-STS lookups to confirm your brand indicators and mail transport security policies are correctly published and compliant.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK 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

Step by step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Mx toolbox account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
2

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.
3

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 Mx toolbox via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

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 Mx toolbox connections to use
5

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 Mx toolbox session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["mx_toolbox"]
    )
    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 Mx toolbox tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

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
7

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 Mx toolbox assistant agent with MCP tools
    agent = AssistantAgent(
        name="mx_toolbox_assistant",
        description="An AI assistant that helps with Mx toolbox 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 Mx toolbox tools from the workbench
8

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Mx toolbox 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 Mx toolbox 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 Mx toolbox 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 Mx toolbox session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["mx_toolbox"]
    )
    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 Mx toolbox assistant agent with MCP tools
        agent = AssistantAgent(
            name="mx_toolbox_assistant",
            description="An AI assistant that helps with Mx toolbox 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 Mx toolbox 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 Mx toolbox 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 Mx toolbox, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

Every Mx toolbox action and event your agent gets out of the box.

Lookup BIMI Record

Retrieves BIMI (Brand Indicators for Message Identification) record and diagnostic information for a domain.

Lookup Blacklist

Tool to perform a blacklist check on a domain or IP.

Lookup DKIM Record

Tool to retrieve DKIM (DomainKeys Identified Mail) records for a domain.

Lookup DMARC Record

Retrieves DMARC (Domain-based Message Authentication, Reporting & Conformance) records for a domain and performs validation checks.

Lookup DNS Records

Performs comprehensive DNS health check and retrieves name server records for a domain.

HTTP Lookup

Tool to perform an HTTP test on a domain.

Lookup MTA-STS Record

Tool to lookup MTA-STS record for a domain.

Lookup MX Records

Retrieves MX (Mail Exchange) records for a domain.

Ping Lookup

Performs a ping test to check network connectivity and measure round-trip time to a domain or IP address.

SMTP Lookup

Tool to perform an SMTP connectivity test on a domain.

Lookup SPF Record

Tool to retrieve SPF records for a specified domain.

Monitor Status

Retrieves the current status of all monitors configured in the MX Toolbox account.

Check Usage

Retrieve API usage statistics for DNS and network lookups.

FRAMEWORKS

How to build Mx toolbox MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Mx toolbox MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Mx toolbox MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Mx toolbox MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Mx toolbox MCP with Claude Cowork

Codex logo

Codex

Use Mx toolbox MCP with Codex

OpenClaw logo

OpenClaw

Use Mx toolbox MCP with OpenClaw

Hermes logo

Hermes

Use Mx toolbox MCP with Hermes

CLI logo

CLI

Use Mx toolbox MCP with CLI

Google ADK logo

Google ADK

Use Mx toolbox MCP with Google ADK

LangChain logo

LangChain

Use Mx toolbox MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Mx toolbox MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Mx toolbox MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Mx toolbox MCP with LlamaIndex

CrewAI logo

CrewAI

Use Mx toolbox MCP with CrewAI

FAQ

Frequently asked questions

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

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 Mx toolbox tools.

Yes, absolutely. You can configure which Mx toolbox 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.

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 Mx toolbox data and credentials are handled as safely as possible.

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