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How to integrate Postmark MCP with Pydantic AI

Connect Pydantic AI to Postmark MCP. Send a password reset email to user, get delivery status for last 10 emails, and more using natural language, with authentication handled for you.

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Postmark is an email delivery service for sending transactional emails with reliable deliverability. It delivers detailed analytics and fast, secure email delivery for developers.

46 Tools

Introduction

This guide walks you through connecting Postmark to Pydantic AI using the Composio tool router. By the end, you'll have a working Postmark agent that can send templated emails in batches, search inbound and outbound messages, track opens and clicks, manage suppressions, and check spam scores through natural language commands.

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

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

For exact tool slugs and parameter tables, see the tool catalog in the Composio docs.

Also integrate Postmark with

Claude CodeClaude CoworkOpenAI Agents SDKClaude Agent SDKGrok BotCodexOpenClawHermesAtomic AgentGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAIAutoGen

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

The Postmark MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Postmark account. It provides structured and secure access to transactional email sending and analytics, so your agent can perform actions like delivering transactional emails, monitoring delivery status, managing templates, and analyzing engagement metrics on your behalf.

  • Automated transactional email delivery: Let your agent send password resets, confirmations, and notification emails with high deliverability and reliability.
  • Template management and customization: Enable your agent to create, update, or select dynamic email templates for consistent, branded communications.
  • Email delivery status monitoring: Ask your agent to track sent messages, check delivery receipts, and identify bounced or failed emails in real time.
  • Engagement and analytics tracking: Have your agent retrieve open and click data, analyze engagement trends, and provide actionable insights from your email campaigns.
  • Suppression list and recipient management: Direct your agent to manage suppression lists, process unsubscribes, and maintain healthy recipient lists automatically.

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

Archive Message Stream

Tool to archive a message stream (soft delete).

Check Spam Score

Assess the spam score of a raw email via the SpamCheck API.

Create Inbound Rule

Tool to create a new inbound rule trigger to block email from a specific sender or domain.

Create Message Stream

Tool to create a new message stream.

Create Suppressions

Tool to add email addresses to the suppression list for a message stream.

Create Template

Tool to create a new email template.

Create Webhook

Tool to create a new webhook configuration for Postmark.

Delete Inbound Rule

Tool to delete a specific inbound rule trigger.

Delete Suppressions

Tool to remove email addresses from the suppression list for a message stream.

Delete Template

Tool to delete a template by its ID or alias.

Delete Webhook

Tool to delete a specific webhook.

Edit Server

Update settings for the current Postmark server.

Edit Template

Tool to update an existing Postmark template by its ID.

Edit Webhook

Update an existing webhook by ID, supplying its URL and triggers.

Get Bounce Counts

Tool to get total counts of emails that have been returned as bounced.

Get Bounces

Tool to retrieve a list of bounces for a server with optional filters.

Get Browser Platform Usage

Retrieve browser platform usage statistics for clicked links, including desktop, mobile, and unknown platform counts.

Get Browser Usage

Retrieve browser usage statistics for clicked links to analyze browsers and platforms used.

Get Click Counts

Tool to retrieve total click counts across all links in emails.

Get Clicks By Browser Family

Tool to retrieve click statistics grouped by browser family.

Get Clicks by Location

Tool to get an overview of which part of the email links were clicked from (HTML or Text).

Get Delivery Stats

Retrieve delivery statistics consisting of inactive recipient counts and bounce counts by type, including spam complaints.

Get Email Client Usage

Retrieve statistics on email clients used to open outbound emails.

Get Email Open Counts

Tool to retrieve counts of opened emails.

Get Message Stream

Tool to retrieve details of a specific message stream by its ID.

Get Opens by Platform

Tool to retrieve email open statistics by platform type.

Get Outbound Overview

Tool to retrieve outbound email statistics overview.

Get Sent Counts

Tool to retrieve total count of emails sent out.

Get Server

Tool to retrieve details of the current Postmark server.

Get Spam Complaints

Tool to retrieve counts of spam complaints.

Get Template

Tool to retrieve details of a specific template by its ID.

Get Tracked Email Counts

Tool to retrieve counts of emails with tracking enabled.

Get Webhook

Tool to retrieve details of a specific webhook by its ID.

List Inbound Rules

Tool to list all inbound rules (triggers) configured for blocking senders.

List Message Streams

Tool to list all message streams for a Postmark server with optional type and archive filtering.

List Outbound Message Clicks

Tool to list clicks for outbound messages with filtering options.

List Outbound Message Opens

Tool to retrieve opens for outbound messages with filtering options.

List Suppressions

Tool to retrieve the suppression list for a message stream with optional filtering.

List Templates

List templates for a Postmark server, with pagination and optional type and active-status filters.

List Webhooks

List current webhook configurations for the Postmark server associated with the authenticated server token.

Search Inbound Messages

Tool to search inbound messages received with optional filtering.

Search Outbound Messages

Tool to search outbound messages with filtering by recipient, tag, status, and date range.

Send Batch Templated Emails

Send up to 500 templated emails in a single batch API call.

Unarchive Message Stream

Tool to unarchive a previously archived message stream.

Update Message Stream

Tool to update a message stream configuration in Postmark.

Validate Template

Tool to validate a Postmark template.

FRAMEWORKS

How to build Postmark MCP Agent with another framework

Claude Code logo

Claude Code

Use Postmark MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Postmark MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Postmark MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Postmark MCP with Claude Agent SDK

Grok Bot logo

Grok Bot

Use Postmark MCP with Grok Bot

Codex logo

Codex

Use Postmark MCP with Codex

OpenClaw logo

OpenClaw

Use Postmark MCP with OpenClaw

Hermes logo

Hermes

Use Postmark MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Postmark MCP with Atomic Agent

Google ADK logo

Google ADK

Use Postmark MCP with Google ADK

LangChain logo

LangChain

Use Postmark MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Postmark MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Postmark MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Postmark MCP with LlamaIndex

CrewAI logo

CrewAI

Use Postmark MCP with CrewAI

AutoGen logo

AutoGen

Use Postmark MCP with AutoGen

FAQ

Frequently asked questions

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