How to Use the VTEX Checkout MCP in Pydantic AI

Q: Can I check client data for VTEX Checkout using Pydantic AI?

Yes, you call getclientprofile. Since the response is validated, you're guaranteed that the fields—like name and email—are present and correctly formatted.

Q: Does VTEX Checkout handle coupon logic with Pydantic AI?

It does. After calling addcoupon, the returned data structure is validated, ensuring you get a reliable, predictable discount value to use in later calculations.

Q: What kind of data does this server touch regarding VTEX Checkout?

This MCP Server deals with client addresses (createaddress), shopping cart details (getorderform), and profile information from getclientprofile.

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Connect VTEX Checkout MCP to Pydantic AI

Create your Vinkius account to connect VTEX Checkout to Pydantic 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 Pydantic AI

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Validating Checkout Transactions

Need to confirm a payment works? The agent calls `simulate_payment` and gets results validated against specific schemas. You can also run `add_coupon` to ensure the discount calculation returns predictable data types.

Managing Client Identity Records

The framework makes managing customer data safe. Use `get_client_profile` to pull user details, or use `create_address` when a new location needs to be saved. Pydantic ensures the structure of that address data is correct every time.

Checking Cart State and Costs

The agent first reads the cart using `get_orderform`. Then, it can run `simulate_order` to predict shipping costs. Because everything's typed, you know exactly what format those cost predictions will come in.

Setup guide

Set up VTEX Checkout MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "vtex-checkout-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to VTEX Checkout tools.",
)

result = await agent.run("List recent VTEX Checkout transactions")
print(result.output)

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Common questions about VTEX Checkout MCP in Pydantic AI

Pydantic AI validates every tool call. When your agent uses `simulate_order` or `add_coupon`, the results are forced into specific Python models, eliminating unexpected data errors.

Yes, you call `get_client_profile`. Since the response is validated, you're guaranteed that the fields—like name and email—are present and correctly formatted.

It does. After calling `add_coupon`, the returned data structure is validated, ensuring you get a reliable, predictable discount value to use in later calculations.

This MCP Server deals with client addresses (`create_address`), shopping cart details (`get_orderform`), and profile information from `get_client_profile`.

The benefit here is safety. If the underlying API returns bad data, your agent fails loudly with a validation error, preventing silent corruption of your business logic.

Use it with your favorite AI tools

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