How to Use the Wherefour MCP in Pydantic AI

Q: How do I use Wherefour with Pydantic AI?

You pass the MCPToolset object as your toolset. The key benefit is that every single function call—like listorders or getorder—is validated against a Pydantic model, failing loudly if data is wrong.

Q: Can I list all items using Wherefour with Pydantic AI?

Yes, you use listinventoryitems. Because of the type-safe approach, you know exactly what structure that list will adhere to. It’s rock solid for production agents.

Guarantee correct data output from Wherefour using your AI client with Pydantic AI.

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Connect Wherefour MCP to Pydantic AI

Create your Vinkius account to connect Wherefour 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 Wherefour with Pydantic AI

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Perplexity

Get structured details on a single item

To check an item, just use the `get_inventory_item` tool. The response is immediately validated against a Pydantic schema, meaning you get clean, predictable data every time. If you need to search broadly, `search_inventory` works too. Because of the type-safe framework, you never have to worry about fields returning unexpected types or missing entirely.

Review vendor and supplier information

When your agent needs a vendor's contact info, it calls `list_vendors`. This data is guaranteed to match the schema, preventing silent corruption in downstream systems. Need to check what they bought? The `list_purchases` tool provides records that are validated at runtime, ensuring you only work with correct dates and amounts.

Manage complex product lists

The framework lets your agent list all sales orders via `list_orders`. This function's output is rigorously checked before the agent uses it. You can then follow up by listing specific inventory items with `list_inventory_items`. This level of validation makes it reliable for mission-critical tasks, letting you trust that every field—from a product ID to an order date—is correctly typed.

Setup guide

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

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

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

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Common questions about Wherefour MCP in Pydantic AI

You pass the `MCPToolset` object as your toolset. The key benefit is that every single function call—like `list_orders` or `get_order`—is validated against a Pydantic model, failing loudly if data is wrong.

It offers correctness. If the underlying API returns unexpected fields or formats, your agent doesn't proceed; it throws a validation error instead. This prevents corrupted data from entering your system.

Yes, you use `list_inventory_items`. Because of the type-safe approach, you know exactly what structure that list will adhere to. It’s rock solid for production agents.

The agent fails immediately and loudly. If the underlying server starts returning data that doesn't fit the expected Pydantic model, your client stops and reports a validation failure. You never risk silent corruption.

The server touches customer invoice and sales order data. The type-safe layer ensures that even when handling sensitive financial records, the structure of the information remains predictable and validated.

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