How to Use the ZIP Codes API MCP in Pydantic AI

Q: How do I search for locations using ZIP Codes API with Pydantic AI?

You can use findzipsbystate to get all codes in a state. The key benefit here is that the resulting list of ZIPs is validated by Pydantic models upon retrieval. This means your agent gets clean, guaranteed data types every time.

Q: Can I use ZIP Codes API with Pydantic AI to find codes in a county?

Yes, the findzipsbycounty tool handles that. When you pass the county name, the resulting list of associated ZIP codes is automatically validated against your Python schema. This adds an extra layer of safety before any code hits your business logic.

Q: What kind of geographic data can I pull with ZIP Codes API and Pydantic AI?

The getzipdetails tool provides the raw geographic and demographic details. With Pydantic, you get a guaranteed structure for this information—you know exactly which fields are present. This makes building complex data pipelines reliable because the types won't change.

Q: Does Pydantic AI support using ZIP Codes API for radius searches?

The findzipsinradius tool finds codes within a specific mile. Since this function is integrated into your workflow, the resulting list of coordinates and codes will be validated by Pydantic. This means the data you use to calculate things like service areas is always correct.

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Setup ZIP Codes API with Pydantic AI

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Getting Zip Details Safely with the MCP Server

Use `get_zip_details` to get geographic and demographic information for a specific ZIP code. Since you're using Pydantic AI, every field returned—like population or boundaries—is validated against your defined model. If the API sends unexpected data, the agent fails loudly with a validation error. You never get silent corruption.

Measuring Distance Between Codes

The `get_zip_distance` tool calculates distance between two ZIP codes. This function call is wrapped in Pydantic validation, ensuring the output structure matches exactly what your application expects. It eliminates guesswork: if the distance calculation fails or returns an invalid type, you know immediately.

Finding Zips by City and State

Need a list of codes for a city? `find_zips_by_city` handles that. Because the response is validated via Pydantic models, your agent doesn't have to worry about schema drift. It guarantees that even when dealing with varied location data, the structure remains predictable.

Setup guide

Set up ZIP Codes API 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": {
        "zip-codes-api-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent ZIP Codes API transactions")
print(result.output)

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Common questions about ZIP Codes API MCP in Pydantic AI

You can use `find_zips_by_state` to get all codes in a state. The key benefit here is that the resulting list of ZIPs is validated by Pydantic models upon retrieval. This means your agent gets clean, guaranteed data types every time.

Yes, the `find_zips_by_county` tool handles that. When you pass the county name, the resulting list of associated ZIP codes is automatically validated against your Python schema. This adds an extra layer of safety before any code hits your business logic.

The `get_zip_details` tool provides the raw geographic and demographic details. With Pydantic, you get a guaranteed structure for this information—you know exactly which fields are present. This makes building complex data pipelines reliable because the types won't change.

The `find_zips_in_radius` tool finds codes within a specific mile. Since this function is integrated into your workflow, the resulting list of coordinates and codes will be validated by Pydantic. This means the data you use to calculate things like service areas is always correct.

The server touches location and demographic details. The agent only receives validated, structured representations of this spatial data. This ensures that while the information is detailed, its format cannot be corrupted by unexpected fields.

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