How to Use the Zippopotam.us Alternative MCP in Pydantic AI

Q: What happens if I use Zippopotam.us Alternative with Pydantic AI?

If either getlocationbypostalcode or getpostalcodesbycity returns data that doesn't match your defined schema, the agent throws a hard validation error immediately. You never get bad data.

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Connect Zippopotam.us Alternative MCP to Pydantic AI

Create your Vinkius account to connect Zippopotam.us Alternative 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 Zippopotam.us Alternative with Pydantic AI

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Validate location by zip code (MCP Server)

The `get_location_by_postal_code` tool lets you pull structured city, state, and coordinate data. Crucially, the Pydantic AI framework validates this output at runtime against your schema. If the API sends unexpected fields, the agent fails loud and clear. No silent corruption.

Validate postal codes from a city (MCP Server)

Need to list all zips for a given city? Use `get_postal_codes_by_city`. The Pydantic validation layer ensures the returned array of zip codes conforms exactly to what your agent expects. This guarantees correctness, which is more important than speed in data processing.

Using Zippopotam.us Alternative with Pydantic AI

The MCP Server provides two specific functions: fetching coordinates from a zip code, and mapping zips from a city/state pair. Passing these tools to the agent ensures that every piece of location data is type-checked. Your agent won't proceed if the underlying structure of the `get_location_by_postal_code` results changes.

Setup guide

Set up Zippopotam.us Alternative 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": {
        "zippopotamus-alternative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Zippopotam.us Alternative transactions")
print(result.output)

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Common questions about Zippopotam.us Alternative MCP in Pydantic AI

It doesn't just pass the data; it validates it against your specific Python models at runtime. If the MCP Server returns a zip code list, Pydantic confirms every element is correct before your agent uses it.

If either `get_location_by_postal_code` or `get_postal_codes_by_city` returns data that doesn't match your defined schema, the agent throws a hard validation error immediately. You never get bad data.

Yes. It handles both finding location details from an existing zip code and generating a list of zips based only on a city/state pair, all while enforcing type safety.

Absolutely. Because the framework prioritizes correctness over everything else, using this MCP Server means you can trust that the location data—the coordinates and zip lists—are structured perfectly.

The server touches geographic and postal code data, specifically retrieving city names, state abbreviations, zip codes, latitude/longitude coordinates, which are all validated against your runtime models.

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