How do I get a HUD User Access Token?

Register for an account at the HUD User API page (https://www.huduser.gov/portal/dataset/uspszip-api.html), confirm your email, and generate a token in your user dashboard.

Which year of data is used?

By default, the API returns the most recent available data. You can optionally specify a year and quarter in each tool.

Does it support mapping Tracts back to ZIP codes?

Yes, tools like tract_to_zip and county_to_zip allow bidirectional crosswalking between geography IDs and ZIP codes.

HUD User (USPS Crosswalk) MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: HUD User (USPS Crosswalk)
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect HUD User (USPS Crosswalk) through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

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Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to HUD User (USPS Crosswalk) "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in HUD User (USPS Crosswalk)?"
    )
    print(result.data)

asyncio.run(main())

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About HUD User (USPS Crosswalk) MCP Server

Empower your AI agents to navigate US geography with precision. This MCP server connects to the HUD User Data API to provide crosswalks between USPS ZIP codes and Census Bureau geographies including Tracts, Counties, CBSAs, and Congressional Districts. Essential for urban planning, demographics analysis, and real estate data processing.

Pydantic AI validates every HUD User (USPS Crosswalk) tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

The HUD User (USPS Crosswalk) MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect HUD User (USPS Crosswalk) to Pydantic AI via MCP

Follow these steps to integrate the HUD User (USPS Crosswalk) MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from HUD User (USPS Crosswalk) with type-safe schemas

Why Use Pydantic AI with the HUD User (USPS Crosswalk) MCP Server

Pydantic AI provides unique advantages when paired with HUD User (USPS Crosswalk) through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your HUD User (USPS Crosswalk) integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your HUD User (USPS Crosswalk) connection logic from agent behavior for testable, maintainable code

HUD User (USPS Crosswalk) + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the HUD User (USPS Crosswalk) MCP Server delivers measurable value.

Type-safe data pipelines: query HUD User (USPS Crosswalk) with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple HUD User (USPS Crosswalk) tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query HUD User (USPS Crosswalk) and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock HUD User (USPS Crosswalk) responses and write comprehensive agent tests

HUD User (USPS Crosswalk) MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect HUD User (USPS Crosswalk) to Pydantic AI via MCP:

cbsa_to_zip

Maps CBSAs to ZIP codes

cbsadiv_to_zip

Maps CBSA Divisions to ZIP codes

cd_to_zip

Maps Congressional Districts to ZIP codes

county_to_zip

Maps Counties to ZIP codes

tract_to_zip

Maps Census Tracts to ZIP codes

zip_to_cbsa

Maps ZIP codes to Core Based Statistical Areas (CBSA)

zip_to_cbsadiv

Maps ZIP codes to CBSA Divisions

zip_to_cd

Maps ZIP codes to Congressional Districts

zip_to_county

Maps ZIP codes to Counties

zip_to_tract

Maps ZIP codes to Census Tracts

Example Prompts for HUD User (USPS Crosswalk) in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with HUD User (USPS Crosswalk) immediately.

"Find the census tracts associated with ZIP code 90210."

"Which ZIP codes are in county FIPS 06037?"

"Get the congressional districts for ZIP code 10001."

Troubleshooting HUD User (USPS Crosswalk) MCP Server with Pydantic AI

Common issues when connecting HUD User (USPS Crosswalk) to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

HUD User (USPS Crosswalk) + Pydantic AI FAQ

Common questions about integrating HUD User (USPS Crosswalk) MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your HUD User (USPS Crosswalk) MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.