How do I get my VTEX API credentials?

Log in to your VTEX Admin, go to **Account Settings → Application Keys**, and create a new App Key. You'll receive an App Key and App Token pair. You also need your Account Name (the subdomain you use to access the VTEX Admin, e.g., 'mystore'). No code, no SDK — just connect and go.

Can my agent simulate a full cart with shipping costs?

Yes! Use the 'simulate_order' tool with a list of items and a postal code. Your agent will return the full cart breakdown — totals, per-item pricing, available logistics options, estimated delivery dates, and applicable discounts — all without touching the actual store.

What happens when a customer asks support about their current cart?

Your support agent can use the 'get_orderform' tool to instantly retrieve the customer's active cart — items, quantities, prices, applied coupons, and shipping address. No need to switch tabs or navigate the admin. The agent can then apply a coupon or check shipping costs, all within the same conversation.

Can I validate payment conditions before placing bulk orders?

Absolutely. The 'simulate_payment' tool lets your agent validate payment tokens and check accepted conditions — perfect for operations teams running bulk order validations across multiple sellers and payment methods without processing real transactions.

VTEX Checkout MCP Server for Pydantic AI 6 tools — connect in under 2 minutes

Name: VTEX Checkout
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 6 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect VTEX Checkout through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to VTEX Checkout "
            "(6 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in VTEX Checkout?"
    )
    print(result.data)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About VTEX Checkout MCP Server

Connect your VTEX e-commerce checkout API to any AI agent and streamline your store's pre-purchase operations through natural conversation.

Pydantic AI validates every VTEX Checkout tool response against typed schemas, catching data inconsistencies at build time. Connect 6 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Cart Simulation — Run complete order simulations with items, quantities, and sellers to instantly preview totals, discounts, and available shipping options for any postal code.
Shopping Cart Management — Retrieve the full state of any active shopping cart (orderform), including items, client profile, payment conditions, and logistics.
Coupon Management — Apply discount coupons to active carts and immediately see the impact on totals.
Client Profiles — Look up registered client profiles by user ID — retrieve name, CPF/CNPJ, email, and contact details.
Address Management — Register new shipping addresses for clients, streamlining the checkout flow.
Payment Simulation — Validate payment tokens and simulate payment conditions before placing an order.

The VTEX Checkout MCP Server exposes 6 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect VTEX Checkout to Pydantic AI via MCP

Follow these steps to integrate the VTEX Checkout MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 6 tools from VTEX Checkout with type-safe schemas

Why Use Pydantic AI with the VTEX Checkout MCP Server

Pydantic AI provides unique advantages when paired with VTEX Checkout through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your VTEX Checkout integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your VTEX Checkout connection logic from agent behavior for testable, maintainable code

VTEX Checkout + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the VTEX Checkout MCP Server delivers measurable value.

Type-safe data pipelines: query VTEX Checkout with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple VTEX Checkout tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query VTEX Checkout and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock VTEX Checkout responses and write comprehensive agent tests

VTEX Checkout MCP Tools for Pydantic AI (6)

These 6 tools become available when you connect VTEX Checkout to Pydantic AI via MCP:

add_coupon

Apply a coupon to a shopping cart

create_address

Add a new address to a client profile

get_client_profile

Get client profile details

get_orderform

Get details of a specific shopping cart

simulate_order

Simulate a cart and shipping costs

simulate_payment

Simulate a payment validation

Example Prompts for VTEX Checkout in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with VTEX Checkout immediately.

"Simulate a cart with 2 units of product ID 1234 and shipping to ZIP 01310-100"

"Apply coupon code SUMMER20 to orderform abc123"

"Look up the client profile for user ID 98765"

Troubleshooting VTEX Checkout MCP Server with Pydantic AI

Common issues when connecting VTEX Checkout to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

VTEX Checkout + Pydantic AI FAQ

Common questions about integrating VTEX Checkout MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your VTEX Checkout MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.