How to integrate Mx toolbox MCP with Pydantic AI

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Introduction

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

This guide will help you understand how to give your Pydantic AI 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.

TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Mx toolbox
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Mx toolbox workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

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.

Supported Tools & Triggers

Tools
Lookup BIMI RecordTool to retrieve bimi (brand indicators for message identification) record for a domain.
Lookup BlacklistTool to perform a blacklist check on a domain or ip.
Lookup DKIM RecordTool to retrieve dkim records for a domain.
Lookup DMARC RecordTool to retrieve dmarc records for a domain.
Lookup DNS RecordsTool to retrieve dns records for a given domain.
HTTP LookupTool to perform an http test on a domain.
Lookup MTA-STS RecordTool to lookup mta-sts record for a domain.
Lookup MX RecordsTool to retrieve mx records for a given domain.
Ping LookupTool to perform a ping test on a specified domain or ip address.
SMTP LookupTool to perform an smtp test on the specified domain.
Lookup SPF RecordTool to retrieve spf records for a specified domain.
Monitor StatusTool to retrieve all monitor statuses.
Check UsageTool to retrieve api usage statistics.

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 with an active API key
  • Basic familiarity with Python and async programming

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 pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Mx toolbox
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file

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

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Mx toolbox
  • MCPServerStreamableHTTP connects to the Mx toolbox MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Mx toolbox
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["mx_toolbox"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Mx toolbox tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
mx_toolbox_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[mx_toolbox_mcp],
    instructions=(
        "You are a Mx toolbox assistant. Use Mx toolbox tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Mx toolbox endpoint
  • The agent uses GPT-5 to interpret user commands and perform Mx toolbox operations
  • The instructions field defines the agent's role and behavior

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Mx toolbox.\n")

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", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Mx toolbox API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Mx toolbox and Pydantic AI:

import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Mx toolbox
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["mx_toolbox"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    mx_toolbox_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[mx_toolbox_mcp],
        instructions=(
            "You are a Mx toolbox assistant. Use Mx toolbox tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Mx toolbox.\n")

    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", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

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

Conclusion

You've built a Pydantic AI agent that can interact with Mx toolbox through Composio's Tool Router. With this setup, your agent can perform real Mx toolbox actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Mx toolbox for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

How to build Mx toolbox MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Mx toolbox MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Mx toolbox MCP with Claude Agent SDK

Google ADK

Google ADK

Use Mx toolbox MCP with Google ADK

LangChain

LangChain

Use Mx toolbox MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Mx toolbox MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Mx toolbox MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Mx toolbox MCP with LlamaIndex

CrewAI

CrewAI

Use Mx toolbox MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Mx toolbox MCP?

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.

Can I use Tool Router MCP with Pydantic AI?

Yes, you can. Pydantic AI 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.

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

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.

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

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