How to integrate Printify MCP with Pydantic AI

This guide walks you through connecting Printify to Pydantic AI using the Composio tool router. By the end, you'll have a working Printify agent that can create draft products, review your orders, calculate shipping, list print providers, and manage webhooks through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Printify account through Composio's Printify MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Printify is a print-on-demand platform for creating and selling custom products through connected merchant shops. It helps teams manage product catalogs, fulfillment, orders, uploads, and webhooks from one place.

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Introduction

This guide walks you through connecting Printify to Pydantic AI using the Composio tool router. By the end, you'll have a working Printify agent that can create draft products, review your orders, calculate shipping, list print providers, and manage webhooks through natural language commands.

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

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

Also integrate Printify 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 Printify
  • 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 Printify 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 Printify MCP server, and what's possible with it?

The Printify MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Printify account. It provides structured and secure access to your shops, catalog, products, orders, images, and webhooks, so your agent can create products, manage listings, review orders, estimate shipping, and monitor shop events on your behalf.

  • Product creation and publishing: Have your agent create product drafts, update product details, publish selected changes to a connected sales channel, mark products as unpublished, or delete products.
  • Catalog and fulfillment research: Let the agent search product blueprints, review blueprint details, compare print providers and variants, and check available shipping methods and costs.
  • Order and shipping review: Direct your agent to list orders, retrieve complete order details, filter orders by fulfillment status or SKU, and calculate shipping estimates for proposed purchases.
  • Shop and image management: Instruct your agent to list your Printify shops, review products, upload artwork to your media library, list uploaded images, or archive images you no longer need.
  • Webhook management and testing: Have your agent create, review, update, delete, and test webhook subscriptions that notify your systems about Printify events.

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

Archive Uploaded Image

Irreversibly archive an image in the connected merchant's Printify media library.

Calculate Order Shipping

Calculate available shipping-cost estimates for proposed line items and a destination.

Create Draft Product

Create a new product draft in a Printify shop.

Create Webhook

Create a persistent webhook subscription for a Printify shop.

Delete Product

Permanently delete a product from a Printify shop.

Delete Webhook

Permanently delete a Printify webhook subscription so future events are no longer delivered.

Get Blueprint

Get the full catalog record for one Printify product blueprint, including its description and images.

Get Order

Get complete details for one Printify order, including its recipient address, line items, status, shipping method, shipments, and timestamps.

Get Product

Get the complete Printify record for one product in a shop, including variants, print areas, images, visibility, and sales-channel properties.

Get Catalog Shipping Details

Get per-variant shipping costs and handling-time ranges for one advertised V2 shipping method.

List Blueprint Print Providers

List print providers that can fulfill a specific Printify blueprint.

List Blueprint Variants

List purchasable variants and print placeholders for a blueprint and print provider.

List Orders

Return one page of orders in a Printify shop, optionally filtered by provider status or exact SKU.

List Products

Return one page of products in a Printify shop.

List Catalog Shipping Methods

List the V2 shipping methods actually available for a Printify blueprint and print provider.

List Shops

List shops available to the connected Printify merchant account.

List Uploaded Images

Return one page of images in the connected merchant's Printify media library.

List Webhooks

List webhook subscriptions configured for a Printify shop.

Publish Product Changes

Start asynchronous publication of one or more fields selected in fields_to_publish from Printify to the shop's connected sales channel.

Search Catalog Blueprints

Search Printify's product blueprint catalog by case-insensitive text in title, brand, or model.

Simulate Webhook Event

Ask Printify to send a simulated event to an existing webhook's external callback URL.

Mark Product Unpublished

Notify Printify that a product has been unpublished from the connected sales channel.

Update Product Draft

Update selected fields of an existing Printify product.

Update Webhook URL

Replace the callback URL of an existing Printify webhook subscription.

Upload Image

Add an image to the connected merchant's Printify media library from either a public HTTP(S) URL or base64 content.

FRAMEWORKS

How to build Printify MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Printify MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Printify MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Printify MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Printify MCP with Claude Cowork

Codex logo

Codex

Use Printify MCP with Codex

OpenClaw logo

OpenClaw

Use Printify MCP with OpenClaw

Hermes logo

Hermes

Use Printify MCP with Hermes

CLI logo

CLI

Use Printify MCP with CLI

Google ADK logo

Google ADK

Use Printify MCP with Google ADK

LangChain logo

LangChain

Use Printify MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Printify MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Printify MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Printify MCP with LlamaIndex

CrewAI logo

CrewAI

Use Printify MCP with CrewAI

FAQ

Frequently asked questions

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

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

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