How to Use the Shopline MCP in Pydantic AI

Q: How do I access order data for Shopline using Pydantic AI?

You use getorderdetails. Since your agent validates every response against a schema, you get reliable, structured transaction data without worrying about bad fields.

Q: Can the Pydantic AI list products for Shopline in a safe way?

Yes. listproducts returns product objects that match your expected Python model at runtime. This is critical for preventing silent data corruption.

Q: Does the Pydantic AI support listing all collections for Shopline?

Yes, listcollections gives you a list of every product grouping. The resulting data structure is guaranteed to be correct and predictable.

Guaranteed Data Correctness for Shopline with Pydantic AI.

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Connect Shopline MCP to Pydantic AI

Create your Vinkius account to connect Shopline to Pydantic AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Shopline with Pydantic AI

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Validate Order Transaction Details

Use `get_order_details` to get precise order data. Because the response is validated by Pydantic, you never risk hallucinated or missing fields—it's guaranteed. The agent can also call `list_orders`, ensuring every field returned for an order adheres strictly to its defined schema.

Verify Product Schema Integrity

Retrieving product data is safe with Pydantic AI. `get_product_details` ensures you get all expected fields, like SKUs and pricing, structured correctly. Calling `list_products` means the agent processes a list where every item matches your defined type model.

Structure Customer Data for Reliability

When listing users with `list_customers`, you get schema-validated customer objects. This prevents runtime errors in downstream Python logic. Furthermore, calling `get_shop_info` guarantees that the returned store data matches expected formats.

Setup guide

Set up Shopline 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": {
        "shopline-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Shopline MCP in Pydantic AI

You use `get_order_details`. Since your agent validates every response against a schema, you get reliable, structured transaction data without worrying about bad fields.

Yes. `list_products` returns product objects that match your expected Python model at runtime. This is critical for preventing silent data corruption.

The server provides access to orders, products, customers, collections, and store information. Every call returns validated JSON that fits your defined schema.

Yes, `list_collections` gives you a list of every product grouping. The resulting data structure is guaranteed to be correct and predictable.

The system provides strict validation for customer records via `list_customers`. This means you know exactly what format the user data will take.

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