How to integrate Unisender MCP with Pydantic AI

This guide walks you through connecting Unisender to Pydantic AI using the Composio tool router. By the end, you'll have a working Unisender agent that can send an sms campaign to new signups, create a new email list segment, check status of yesterday's email campaign through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Unisender account through Composio's Unisender MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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UniSender is a service for email and SMS marketing campaigns. It helps you reach your audience easily and automate campaign management.

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Introduction

This guide walks you through connecting Unisender to Pydantic AI using the Composio tool router. By the end, you'll have a working Unisender agent that can send an sms campaign to new signups, create a new email list segment, check status of yesterday's email campaign through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a Unisender account through Composio's Unisender MCP server.

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

Also integrate Unisender with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

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 Unisender
  • 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 Unisender 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 Unisender MCP server, and what's possible with it?

The Unisender MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Unisender account. It provides structured and secure access so your agent can perform Unisender operations on your behalf.

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 step09 STEPS
1

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
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 pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Unisender
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

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
5

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 Unisender
  • MCPServerStreamableHTTP connects to the Unisender MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

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 Unisender
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["unisender"],
    )
    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 Unisender 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
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
unisender_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[unisender_mcp],
    instructions=(
        "You are a Unisender assistant. Use Unisender tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Unisender endpoint
  • The agent uses GPT-5 to interpret user commands and perform Unisender operations
  • The instructions field defines the agent's role and behavior
8

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 Unisender.\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
  • Unisender API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

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 Unisender and Pydantic AI:

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()

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 Unisender
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["unisender"],
    )
    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
    unisender_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[unisender_mcp],
        instructions=(
            "You are a Unisender assistant. Use Unisender 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 Unisender.\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 Unisender through Composio's Tool Router. With this setup, your agent can perform real Unisender 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 + Unisender for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
TOOLS

Supported Tools

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

Check Email Status

Tool to check the delivery status of emails sent via sendEmail method.

Create Email Template

Tool to create a new email template for mass campaigns in UniSender.

Create Field

Tool to create a new custom field for contact data in UniSender.

Create List

Tool to create a new contact list for organizing thematic email campaigns in UniSender.

Create Subscriber Note

Tool to create a note for a subscriber contact in UniSender.

Delete Field

Tool to remove a custom field from the Unisender system.

Delete List

Tool to permanently delete a contact list from your UniSender account.

Delete Subscriber Note

Tool to delete a subscriber note from UniSender by its ID.

Delete Tag

Tool to delete a tag by its ID and remove it from all associated contacts.

Delete Template

Tool to remove a template from the UniSender account.

Exclude Contact from Lists

Tool to remove contacts from specified lists or all lists in UniSender.

Export Contacts

Tool to export contact data from UniSender lists for synchronization.

Get Campaigns

Tool to retrieve list of campaigns from Unisender within a specified date range.

Get Campaign Status

Tool to check the current status of a campaign in UniSender.

Get Contact

Tool to get information about a single contact from UniSender.

Get Contact Field Values

Tool to retrieve custom field values for a specific contact identified by email address.

Get Fields

Tool to retrieve all custom user-defined fields for contact personalization and data management.

Get Lists

Tool to retrieve all existing mailing lists associated with the account.

Get Messages

Tool to retrieve list of all messages with body and attachments.

Get Sender Domain List

Tool to retrieve information about sender domains and their DKIM status.

Get Tags

Tool to retrieve all custom tags/labels for contact segmentation.

Get Template

Tool to retrieve detailed information about a specific email template by its ID.

Get Templates

Tool to retrieve list of all templates with full content including body.

Import Contacts (Bulk)

Tool to bulk import contacts to UniSender with maximum 500 contacts per call.

Check if Contact is in Lists

Tool to check if a contact exists in specified mailing lists based on and/or conditions.

List Messages

Tool to list all messages without body and attachments.

List Templates

Tool to list email templates without body content.

Subscribe Contact to Lists

Tool to add contacts to one or multiple mailing lists with optional tags and field values.

Unsubscribe Contact

Tool to unsubscribe contacts from mailing lists in UniSender.

Update Email Template

Tool to update an existing email template for mass campaigns.

Update Field

Tool to modify parameters of an existing custom field in UniSender.

Update List

Tool to update the parameters of an existing contact list in UniSender.

Update Subscriber Note

Tool to update the content of an existing subscriber note in UniSender.

FRAMEWORKS

How to build Unisender MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Unisender MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Unisender MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Unisender MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Unisender MCP with Claude Cowork

Codex logo

Codex

Use Unisender MCP with Codex

OpenClaw logo

OpenClaw

Use Unisender MCP with OpenClaw

Hermes logo

Hermes

Use Unisender MCP with Hermes

CLI logo

CLI

Use Unisender MCP with CLI

Google ADK logo

Google ADK

Use Unisender MCP with Google ADK

LangChain logo

LangChain

Use Unisender MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Unisender MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Unisender MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Unisender MCP with LlamaIndex

CrewAI logo

CrewAI

Use Unisender MCP with CrewAI

FAQ

Frequently asked questions

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

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 Unisender tools.

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

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Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

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Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

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Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

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CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

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Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

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Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

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Jerome Leclanche
Co-Founder, Ingram Technologies