How to Use the Zakeke MCP in Mastra AI

Q: How do I start listing products using Zakeke with Mastra AI?

You call the listproducts tool. Because you're building a complex workflow, Mastra AI executes this reliably, treating it as the initial step in your process.

Q: What happens if I need to check order details using Zakeke with Mastra AI?

You use getorderdetails. If the connection is spotty, Mastra AI's built-in retries will automatically attempt to fetch the data until it succeeds or hits a hard limit.

Q: Can Mastra AI handle listing customer designs from Zakeke?

Yes. You use listdesigns. The workflow engine treats this list as input for subsequent steps, making sure the data is available even if earlier parts of the process fail.

Q: What data types does the Zakeke MCP Server touch regarding privacy?

This server touches designdetails and specific product information. You'll be dealing with configuration metrics, so ensure your Mastra AI workflow handles state transitions correctly.

Q: How do I list all customized orders using Zakeke with Mastra AI?

Use listorders. Your Mastra AI agent can process this list and then use conditional branching—for example, checking if any listed order needs immediate payment follow-up.

Build complex e-commerce workflows and handle failures with Mastra AI.

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Setup Zakeke with Mastra AI

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List all available products for workflow start

Start any workflow by listing the product options. The `list_products` tool gets every item that can be customized, giving your agent a solid foundation to build off of. This is key for Mastra AI because if an initial check fails—like if no products are listed—the workflow engine knows exactly how to branch and retry.

Verify composition data in multi-step processes

Need to confirm a specific 3D build? `get_composition_details` grabs all the necessary specs for that configuration. This is critical when you're running conditional logic. Mastra AI handles this gracefully; if the initial call fails, it can automatically retry using exponential backoff until it gets valid composition data.

Generate and retrieve print files reliably

The final step is getting the printable assets. Call `get_design_print_files` to get a ZIP of print-ready outputs for a design. This operation must succeed. Mastra AI's workflow engine ensures that if file generation fails, it can notify an admin and automatically trigger a fallback process instead of just crashing.

Setup guide

Set up Zakeke MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Zakeke tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "zakeke-mcp-client",
  servers: {
    "zakeke-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Zakeke Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Zakeke tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Zakeke transactions"
);
console.log(result.text);

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Common questions about Zakeke MCP in Mastra AI

You call the `list_products` tool. Because you're building a complex workflow, Mastra AI executes this reliably, treating it as the initial step in your process.

You use `get_order_details`. If the connection is spotty, Mastra AI's built-in retries will automatically attempt to fetch the data until it succeeds or hits a hard limit.

Yes. You use `list_designs`. The workflow engine treats this list as input for subsequent steps, making sure the data is available even if earlier parts of the process fail.

This server touches `design_details` and specific product information. You'll be dealing with configuration metrics, so ensure your Mastra AI workflow handles state transitions correctly.

Use `list_orders`. Your Mastra AI agent can process this list and then use conditional branching—for example, checking if any listed order needs immediate payment follow-up.

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