How to integrate Printify MCP with Vercel AI SDK v6

This guide walks you through connecting Printify to Vercel AI SDK v6 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 Vercel AI SDK 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.

25 Tools

Introduction

This guide walks you through connecting Printify to Vercel AI SDK v6 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 Vercel AI SDK 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 ADKLangChainMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • How to set up and configure a Vercel AI SDK agent with Printify integration
  • Using Composio's Tool Router to dynamically load and access Printify tools
  • Creating an MCP client connection using HTTP transport
  • Building an interactive CLI chat interface with conversation history management
  • Handling tool calls and results within the Vercel AI SDK framework

What is Vercel AI SDK?

The Vercel AI SDK is a TypeScript library for building AI-powered applications. It provides tools for creating agents that can use external services and maintain conversation state.

Key features include:

  • streamText: Core function for streaming responses with real-time tool support
  • MCP Client: Built-in support for Model Context Protocol via @ai-sdk/mcp
  • Step Counting: Control multi-step tool execution with stopWhen: stepCountIs()
  • OpenAI Provider: Native integration with OpenAI models

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 you begin, make sure you have:
  • Node.js and npm installed
  • A Composio account with API key
  • An OpenAI API key
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 required dependencies

bash
npm install @ai-sdk/openai @ai-sdk/mcp @composio/core ai dotenv

First, install the necessary packages for your project.

What you're installing:

  • @ai-sdk/openai: Vercel AI SDK's OpenAI provider
  • @ai-sdk/mcp: MCP client for Vercel AI SDK
  • @composio/core: Composio SDK for tool integration
  • ai: Core Vercel AI SDK
  • dotenv: Environment variable management
4

Set up environment variables

bash
OPENAI_API_KEY=your_openai_api_key_here
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here

Create a .env file in your project root.

What's needed:

  • OPENAI_API_KEY: Your OpenAI API key for GPT model access
  • COMPOSIO_API_KEY: Your Composio API key for tool access
  • COMPOSIO_USER_ID: A unique identifier for the user session
5

Import required modules and validate environment

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Composio } from "@composio/core";
import * as readline from "readline";
import { streamText, type ModelMessage, stepCountIs } from "ai";
import { createMCPClient } from "@ai-sdk/mcp";

const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey,
});
What's happening:
  • We're importing all necessary libraries including Vercel AI SDK's OpenAI provider and Composio
  • The dotenv/config import automatically loads environment variables
  • The MCP client import enables connection to Composio's tool server
6

Create Tool Router session and initialize MCP client

typescript
async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["printify"],
  });

  const mcpUrl = session.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 mcp object contains the URL and authentication headers needed to connect to the MCP server
  • This session provides access to all Printify-related tools through the MCP protocol
7

Connect to MCP server and retrieve tools

typescript
const mcpClient = await createMCPClient({
  transport: {
    type: "http",
    url: mcpUrl,
    headers: session.mcp.headers, // Authentication headers for the Composio MCP server
  },
});

const tools = await mcpClient.tools();
What's happening:
  • We're creating an MCP client that connects to our Composio Tool Router session via HTTP
  • The mcp.url provides the endpoint, and mcp.headers contains authentication credentials
  • The type: "http" is important - Composio requires HTTP transport
  • tools() retrieves all available Printify tools that the agent can use
8

Initialize conversation and CLI interface

typescript
let messages: ModelMessage[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log(
  "Ask any questions related to printify, like summarize my last 5 emails, send an email, etc... :)))\n",
);

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: "> ",
});

rl.prompt();
What's happening:
  • We initialize an empty messages array to maintain conversation history
  • A readline interface is created to accept user input from the command line
  • Instructions are displayed to guide the user on how to interact with the agent
9

Handle user input and stream responses with real-time tool feedback

typescript
rl.on("line", async (userInput: string) => {
  const trimmedInput = userInput.trim();

  if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
    console.log("\nGoodbye!");
    rl.close();
    process.exit(0);
  }

  if (!trimmedInput) {
    rl.prompt();
    return;
  }

  messages.push({ role: "user", content: trimmedInput });
  console.log("\nAgent is thinking...\n");

  try {
    const stream = streamText({
      model: openai("gpt-5"),
      messages,
      tools,
      toolChoice: "auto",
      stopWhen: stepCountIs(10),
      onStepFinish: (step) => {
        for (const toolCall of step.toolCalls) {
          console.log(`[Using tool: ${toolCall.toolName}]`);
          }
          if (step.toolCalls.length > 0) {
            console.log(""); // Add space after tool calls
          }
        },
      });

      for await (const chunk of stream.textStream) {
        process.stdout.write(chunk);
      }

      console.log("\n\n---\n");

      // Get final result for message history
      const response = await stream.response;
      if (response?.messages?.length) {
        messages.push(...response.messages);
      }
    } catch (error) {
      console.error("\nAn error occurred while talking to the agent:");
      console.error(error);
      console.log(
        "\nYou can try again or restart the app if it keeps happening.\n",
      );
    } finally {
      rl.prompt();
    }
  });

  rl.on("close", async () => {
    await mcpClient.close();
    console.log("\n👋 Session ended.");
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});
What's happening:
  • We use streamText instead of generateText to stream responses in real-time
  • toolChoice: "auto" allows the model to decide when to use Printify tools
  • stopWhen: stepCountIs(10) allows up to 10 steps for complex multi-tool operations
  • onStepFinish callback displays which tools are being used in real-time
  • We iterate through the text stream to create a typewriter effect as the agent responds
  • The complete response is added to conversation history to maintain context
  • Errors are caught and displayed with helpful retry suggestions

Complete Code

Here's the complete code to get you started with Printify and Vercel AI SDK:

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Composio } from "@composio/core";
import * as readline from "readline";
import { streamText, type ModelMessage, stepCountIs } from "ai";
import { createMCPClient } from "@ai-sdk/mcp";

const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey,
});

async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["printify"],
  });

  const mcpUrl = session.mcp.url;

  const mcpClient = await createMCPClient({
    transport: {
      type: "http",
      url: mcpUrl,
      headers: session.mcp.headers, // Authentication headers for the Composio MCP server
    },
  });

  const tools = await mcpClient.tools();

  let messages: ModelMessage[] = [];

  console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
  console.log(
    "Ask any questions related to printify, like summarize my last 5 emails, send an email, etc... :)))\n",
  );

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
      console.log("\nGoodbye!");
      rl.close();
      process.exit(0);
    }

    if (!trimmedInput) {
      rl.prompt();
      return;
    }

    messages.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    try {
      const stream = streamText({
        model: openai("gpt-5"),
        messages,
        tools,
        toolChoice: "auto",
        stopWhen: stepCountIs(10),
        onStepFinish: (step) => {
          for (const toolCall of step.toolCalls) {
            console.log(`[Using tool: ${toolCall.toolName}]`);
          }
          if (step.toolCalls.length > 0) {
            console.log(""); // Add space after tool calls
          }
        },
      });

      for await (const chunk of stream.textStream) {
        process.stdout.write(chunk);
      }

      console.log("\n\n---\n");

      // Get final result for message history
      const response = await stream.response;
      if (response?.messages?.length) {
        messages.push(...response.messages);
      }
    } catch (error) {
      console.error("\nAn error occurred while talking to the agent:");
      console.error(error);
      console.log(
        "\nYou can try again or restart the app if it keeps happening.\n",
      );
    } finally {
      rl.prompt();
    }
  });

  rl.on("close", async () => {
    await mcpClient.close();
    console.log("\n👋 Session ended.");
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});

Conclusion

You've successfully built a Printify agent using the Vercel AI SDK with streaming capabilities! This implementation provides a powerful foundation for building AI applications with natural language interfaces and real-time feedback.

Key features of this implementation:

  • Real-time streaming responses for a better user experience with typewriter effect
  • Live tool execution feedback showing which tools are being used as the agent works
  • Dynamic tool loading through Composio's Tool Router with secure authentication
  • Multi-step tool execution with configurable step limits (up to 10 steps)
  • Comprehensive error handling for robust agent execution
  • Conversation history maintenance for context-aware responses

You can extend this further by adding custom error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.
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

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. Vercel AI SDK v6 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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