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How to Use the VTEX Checkout MCP in OpenAI Agents SDK

Build production-grade e-commerce logic with the OpenAI Agents SDK.

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OpenAI Agents SDK

Connect VTEX Checkout MCP to OpenAI Agents SDK

Create your Vinkius account to connect VTEX Checkout to OpenAI Agents SDK 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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Process Checkout Data via MCP Server

Need to know if a customer can afford it? The agent handles complex simulations. You use `simulate_order` to get total cart costs, and then run `simulate_payment` against those figures. This lets your system check both shipping rates and payment eligibility before finalizing anything.

Managing Customer Details

The agent keeps track of who the user is. It can fetch existing details using `get_client_profile`, or if they're new, it runs `create_address` so you don't have to ask for everything twice. This makes the checkout process much smoother.

Applying Promotions and Coupons

Don't let customers forget their discount. You run `add_coupon` with a specific code, which applies the percentage or fixed amount right to the current cart total. This tool handles the calculation so your agent knows exactly what the final price should be.

Setup guide

Set up VTEX Checkout MCP in OpenAI Agents SDK

Prerequisites

  • Python 3.10+ installed
  • openai-agents package (pip install openai-agents)
  • Active Vinkius subscription with a valid endpoint token
  1. 1

    Install the SDK

    Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.

  2. 2

    Connect via SSE transport

    Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all VTEX Checkout tools at runtime.

  3. 3

    Create your Agent

    Pass the MCP to Agent(mcp_servers=[server]). The agent receives VTEX Checkout tools as native definitions — JSON schemas resolve automatically.

  4. 4

    Run the agent

    Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate VTEX Checkout tools and returns structured results. Copy the full example on the right to get started.

agent.py
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="VTEX Checkout Agent",
            instructions="You have access to VTEX Checkout tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about VTEX Checkout MCP in OpenAI Agents SDK

The OpenAI Agents SDK treats the checkout process as a set of callable tools. Your agent decides when to use `get_orderform` to read the current cart state, and then calls other tools like `simulate_payment` to validate it.
Yeah. You call `get_client_profile` first; that gives your agent all the necessary details about the user's existing account, like saved addresses or past orders.
Absolutely. You send a coupon code to `add_coupon`, and the MCP Server figures out if it applies, how much it discounts, and updates the cart total in real-time.
This MCP Server primarily interacts with shopping cart data, customer addresses (`create_address`), and client profile information (`get_client_profile`).
Make sure your agent correctly handles the sequence. You need to read the cart (`get_orderform`) before attempting to simulate payment or apply coupons. The order matters.

Start using the VTEX Checkout MCP today

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