How to Use the VTEX Checkout MCP in Mastra AI

Build resilient, multi-step VTEX checkout workflows with Mastra AI.

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Connect VTEX Checkout MCP to Mastra AI

Create your Vinkius account to connect VTEX Checkout to Mastra AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup VTEX Checkout with Mastra AI

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Perplexity

Complex Checkout Logic on the MCP Server

The `simulate_order` tool allows you to build complex transaction logic that first calculates shipping costs and then determines final totals. This is perfect for multi-step, failure-resistant workflows. If a workflow requires knowing who the shopper is, the agent pulls details using `get_client_profile`. These tools work together in sequence, ensuring data integrity.

Handling VTEX Checkout with Mastra AI

Mastra AI uses the `simulate_payment` tool to validate payment methods. If validation fails, your workflow can automatically trigger a retry using an alternative method or notify an admin—all without human intervention. Need to adjust the cart? The agent can use `add_coupon` and then run `get_orderform` again to confirm the price change.

Client Profile Management via MCP Server

When a user needs to provide an address, the workflow uses `create_address`. This tool reliably adds new location data to the client record. If you need to verify existing details before checkout, the agent calls `get_client_profile` first. This guarantees that subsequent steps use accurate, up-to-date information.

Setup guide

Set up VTEX Checkout 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 VTEX Checkout 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: "vtex-checkout-mcp-client",
  servers: {
    "vtex-checkout-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

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

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Common questions about VTEX Checkout MCP in Mastra AI

Mastra AI's built-in workflow engine handles failure gracefully. If `simulate_payment` fails, the agent can automatically retry with a different method or escalate to an admin alert.

Your workflows primarily require client profile details and current cart status. The MCP Server gives you access to `get_client_profile` and `get_orderform` data.

Absolutely. You can create a sequence: first, get the client profile; second, simulate an order; third, apply a coupon using `add_coupon`, and finally, run payment simulation.

The server touches client profile details. You must implement conditional branching logic around any use of `get_client_profile` to ensure sensitive PII is only accessed and processed when absolutely necessary.

If a user provides a new shipping address during checkout, the agent uses `create_address`. This tool ensures the location data is correctly mapped to the client record for future orders.

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