How to integrate Benchmark email MCP with Pydantic AI

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Introduction

This guide walks you through connecting Benchmark email to Pydantic AI using the Composio tool router. By the end, you'll have a working Benchmark email agent that can list all confirmed sender email addresses, get my benchmark account plan details, fetch company profile and contact limits, retrieve all current account settings through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a Benchmark email account through Composio's Benchmark email 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 Benchmark email
  • 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 Benchmark email 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 Benchmark email MCP server, and what's possible with it?

The Benchmark email MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Benchmark Email account. It provides structured and secure access to your email marketing data, so your agent can retrieve account info, manage contacts, handle lists, and automate campaign administration on your behalf.

  • Automated contact and list management: Effortlessly add, update, or delete contacts and lists, keeping your subscriber base organized and up to date.
  • Campaign cleanup and maintenance: Direct your agent to delete obsolete email campaigns or remove unneeded webhooks to keep your workspace tidy.
  • Account insights and configuration retrieval: Have the agent fetch client details, plan information, and account settings—perfect for reporting or reviewing your workspace setup.
  • Confirmed email address retrieval: Quickly pull all verified sender email addresses for compliance and seamless campaign sending.
  • Agency account and webhook control: Manage linked agency accounts and webhooks by deleting or updating them when no longer needed for more secure integrations.

Supported Tools & Triggers

Tools
Delete Contact From ListTool to delete a contact from a specific list by contactid.
Delete Contact ListTool to delete a contact list.
Delete Email CampaignTool to delete an email campaign.
Delete Linked Agency AccountTool to delete a linked agency account.
Delete WebhookTool to delete a webhook from a contact list by its id.
Get All Confirmed EmailsTool to retrieve all confirmed email addresses for the client account.
Get Client Account SettingsTool to get client account settings such as company, language, timezone, and sender info.
Get client detailsTool to get client details including profile data, contact count, and plan information.
Get Client Plan InformationTool to get client's plan information including addons, email plan, and total contacts.
Get client profile detailsTool to get client's profile details like business city, country, phone, and company.
Get Contact List DetailsTool to fetch detailed information for a contact list.
Get Contact ListsTool to retrieve all contact lists.
Get Filtered Contacts in ListTool to fetch filtered and paginated contacts from a list by listid.
Get Email Report ForwardsTool to get forwards report for an email campaign.
Get Unopens ReportTool to get unopens report for an email campaign by id.
Get Linked Agency Account DetailsTool to get details of a linked agency account.
Get Linked Agency AccountsTool to get list of linked agency accounts.
Get sub-account detailsTool to get details for a specific sub-account by id.
Get Sub-Account HistoryTool to get sub-account history.
Get Sub-AccountsTool to retrieve all sub-accounts for the client.
Get Sub-Accounts Plan ListTool to retrieve available plans for a sub-account.
Change PasswordTool to change the password for the client account.
Save Security PINTool to save a new security pin for the client account.
Send Reset EmailTool to send a reset email link to change the primary email address.
Patch Update Client SettingsTool to update client account settings.
Update Contact ListTool to update an existing contact list.
Update/Edit ProfileTool to update or edit profile information such as first name, last name, and phone number.
Update WebhookTool to update a webhook for a contact list by webhook id.
Add Contact to ListTool to add a new contact to a specific list.
Change Security PINTool to change security pin for the client account.
Create Contact ListTool to create a new contact list.
Create WebhookTool to create a new webhook for a contact list.
Disable Security PINTool to disable security pin for the client account.
Save Website DomainTool to save a website domain for your benchmark email account.
Send Confirm Email VerificationTool to send confirm email verification.
Send PIN via EmailTool to send pin via email.

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 Benchmark email
  • 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 Benchmark email
  • MCPServerStreamableHTTP connects to the Benchmark email 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 Benchmark email
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["benchmark_email"],
    )
    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 Benchmark email 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
benchmark_email_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[benchmark_email_mcp],
    instructions=(
        "You are a Benchmark email assistant. Use Benchmark email tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Benchmark email endpoint
  • The agent uses GPT-5 to interpret user commands and perform Benchmark email 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 Benchmark email.\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
  • Benchmark email 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 Benchmark email 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 Benchmark email
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["benchmark_email"],
    )
    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
    benchmark_email_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[benchmark_email_mcp],
        instructions=(
            "You are a Benchmark email assistant. Use Benchmark email 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 Benchmark email.\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 Benchmark email through Composio's Tool Router. With this setup, your agent can perform real Benchmark email 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 + Benchmark email 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 Benchmark email MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Benchmark email MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Benchmark email MCP with Claude Agent SDK

Google ADK

Google ADK

Use Benchmark email MCP with Google ADK

LangChain

LangChain

Use Benchmark email MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Benchmark email MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Benchmark email MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Benchmark email MCP with LlamaIndex

CrewAI

CrewAI

Use Benchmark email MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Benchmark email MCP?

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

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

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

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