How to Use the Zakeke MCP in Pydantic AI

Q: How does Zakeke provide reliable order data using Pydantic AI?

The agent calls getorderdetails to pull specific transaction records. Since Pydantic AI validates the response, you're guaranteed that the returned fields—like item ID or price—match your expected data types.

Q: Can I list product details safely using Zakeke and Pydantic AI?

Yes. Calling listproducts returns structured data, and Pydantic AI verifies that every entry conforms to the defined schema. This is key for production reliability.

Q: How does Zakeke handle cart information using Pydantic AI?

You use getcartinfo. The framework validates the entire payload, ensuring that every component listed in the 3D configuration matches your models. This stops silent data corruption.

Q: What happens if I need print files using Zakeke and Pydantic AI?

While getdesignprintfiles returns a ZIP, the metadata surrounding that file (like version or required format) will be validated. This ensures you know exactly what kind of output you're getting.

Q: Does Zakeke help discover designs using Pydantic AI?

Absolutely. When listing all designs via listdesigns, the agent confirms that each design record contains all necessary fields (like owner ID and creation date) before passing it to your application logic.

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Connect Zakeke MCP to Pydantic AI

Create your Vinkius account to connect Zakeke 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 Zakeke with Pydantic AI

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Validate Design Data with MCP Server

`get_design_details` lets your agent fetch specific design data. Because of runtime validation, if the API returns a field that shouldn't be there—like an extra metadata tag—the agent fails loudly instead of silently using bad data. You can also validate output from `get_cart_info`, ensuring the cart structure matches exactly what your Python models expect.

Process and Validate Orders with MCP Server

`list_orders` gives you a list of orders, but Pydantic AI validates that every item in that list has the expected fields. This prevents bad data from corrupting your downstream systems. Use `get_order_details` to fetch specifics; if the order object is malformed, the agent throws an immediate validation error.

Manage Product Catalogs with MCP Server

`list_products` and `list_compositions` let your agent build a full picture of what you sell. If one of these lists suddenly returns data in the wrong format, Pydantic AI catches it before execution. This level of type safety means that when you call `list_designs`, you get exactly the structure you defined.

Setup guide

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

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

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

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Common questions about Zakeke MCP in Pydantic AI

The agent calls `get_order_details` to pull specific transaction records. Since Pydantic AI validates the response, you're guaranteed that the returned fields—like item ID or price—match your expected data types.

Yes. Calling `list_products` returns structured data, and Pydantic AI verifies that every entry conforms to the defined schema. This is key for production reliability.

You use `get_cart_info`. The framework validates the entire payload, ensuring that every component listed in the 3D configuration matches your models. This stops silent data corruption.

While `get_design_print_files` returns a ZIP, the metadata surrounding that file (like version or required format) will be validated. This ensures you know exactly what kind of output you're getting.

Absolutely. When listing all designs via `list_designs`, the agent confirms that each design record contains all necessary fields (like owner ID and creation date) before passing it to your application logic.

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