How to Use the Shoplazza MCP in Pydantic AI

Q: What operations are available using Shoplazza and Pydantic AI?

You can create new customers with createshoplazzacustomer, get product details via getshoplazzaproduct, or list orders using listshoplazzaorders. The types make these interactions reliable.

Q: If I need to view customer lists, which tool do I use with Shoplazza and Pydantic AI?

Start by running listshoplazzacustomers. This provides a validated list of all existing user IDs for your agent to process further.

Shoplazza: Building Type-Safe E-commerce Agents with Pydantic AI.

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Setup Shoplazza with Pydantic AI

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Creating and Updating Products via MCP Server

Adding a new item? You use `create_shoplazza_product`. The framework ensures that every field you send matches the expected schema, preventing silent data corruption. Similarly, to update product info, you get details using `get_shoplazza_product`. To see what products exist, run `list_shoplazza_products`. If you need a full inventory check, use `list_shoplazza_collections` to understand the categorization.

Retrieving Customer and Order Details

Want customer history? The agent can call `get_shoplazza_customer` after fetching IDs via `list_shoplazza_customers`. Because of Pydantic validation, you'll always know exactly what fields are present. For orders, run `list_shoplazza_orders`. You then pass an ID to `get_shoplazza_order` to get the full transaction record. The type safety makes auditing simple.

Getting Shop and Catalog Context

You can pull overall shop context using `get_shoplazza_shop`. This tool provides the foundational metadata needed for any operation. If you're mapping out what collections exist, use `list_shoplazza_collections`. It gives a validated list of groups, which helps your agent structure complex reports about Shoplazza.

Setup guide

Set up Shoplazza 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": {
        "shoplazza-1-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Shoplazza MCP in Pydantic AI

Pydantic validation guarantees that every MCP response adheres to a strict Python schema. If the API returns unexpected fields, your agent fails loudly, meaning you know exactly where the corruption came from.

You can create new customers with `create_shoplazza_customer`, get product details via `get_shoplazza_product`, or list orders using `list_shoplazza_orders`. The types make these interactions reliable.

Yes. It's built for cross-border selling, managing multi-currency data and localized checkout flows across many regions using structured tools.

Start by running `list_shoplazza_customers`. This provides a validated list of all existing user IDs for your agent to process further.

The server manages typed e-commerce resources, including structured product records, customer profiles, shop metadata, and order details. Everything is validated against models.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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