How to Use the U.S. Census Housing — Home Values, Rent & Real Estate Data MCP in Pydantic AI

Q: How does Pydantic AI access U.S. Census Housing — Home Values, Rent & Real Estate Data?

You can use gethousingbycounty to get housing data for all counties in a state. The Pydantic framework guarantees the resulting object conforms to your expected schema.

Q: What variables are available with U.S. Census Housing — Home Values, Rent & Real Estate Data using Pydantic AI?

You can use querycensus to specify any ACS variable needed, covering everything from population counts to detailed housing metrics.

Q: Can Pydantic AI retrieve vacancy rates with U.S. Census Housing — Home Values, Rent & Real Estate Data?

Yes. The gethousingbystate tool provides renter-occupied and vacant units for all states, ensuring the data you get is validated upon receipt.

Q: Does U.S. Census Housing — Home Values, Rent & Real Estate Data support filtering with Pydantic AI?

The querycensus tool supports optional in-clause filtering for both geography and demographics, which is then validated by the framework.

Get type-safe U.S. Census Housing — Home Values, Rent & Real Estate Data responses using Pydantic AI.

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Connect U.S. Census Housing — Home Values, Rent & Real Estate Data MCP to Pydantic AI

Create your Vinkius account to connect U.S. Census Housing — Home Values, Rent & Real Estate Data 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 U.S. Census Housing — Home Values, Rent & Real Estate Data with Pydantic AI

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Structured County Analysis with the MCP Server

Use `get_county_profile` to get a profile for any county. Because of Pydantic validation, you know exactly what fields you're getting—no surprises, no silent corruption. This is critical when your agent needs to process specific metrics like population totals alongside median rent data.

Reliable State-Level Data Retrieval with the MCP Server

The `get_state_profile` tool gives a complete socioeconomic picture for a state. The Pydantic layer ensures that even if the API output is messy, your agent's model receives predictable data structures. It lets you confidently compare housing rates and demographics across multiple states.

Validated Custom Census Queries with the MCP Server

Running `query_census` means accessing any dataset variable, but Pydantic adds a necessary check. If the census API returns an unexpected field, your agent fails loudly—you know it's wrong. This focus on correctness is perfect for high-stakes financial or research applications.

Setup guide

Set up U.S. Census Housing — Home Values, Rent & Real Estate Data 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": {
        "us-census-housing-home-values-rent-real-estate-data-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to U.S. Census Housing — Home Values, Rent & Real Estate Data tools.",
)

result = await agent.run("List recent U.S. Census Housing — Home Values, Rent & Real Estate Data transactions")
print(result.output)

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Common questions about U.S. Census Housing — Home Values, Rent & Real Estate Data MCP in Pydantic AI

You can use `get_housing_by_county` to get housing data for all counties in a state. The Pydantic framework guarantees the resulting object conforms to your expected schema.

You can use `query_census` to specify any ACS variable needed, covering everything from population counts to detailed housing metrics.

Yes. The `get_housing_by_state` tool provides renter-occupied and vacant units for all states, ensuring the data you get is validated upon receipt.

The `query_census` tool supports optional in-clause filtering for both geography and demographics, which is then validated by the framework.

This server touches residential real estate data. We pull median home values, gross rent estimates, and ownership vs. renting rates at state and county levels.

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