How to Use the HUD User (USPS Crosswalk) MCP in Google ADK

Q: Can Google ADK restrict which HUD User (USPS Crosswalk) tools are active?

You can use the toolnames filter in the ADK to limit exposure. If your agent only needs county-level data, you restrict it to just ziptocounty and countytozip.

Q: Will this work with Gemini's long context window?

Yes. When a tool like countytozip returns hundreds of postal codes, Gemini easily holds the entire JSON response in context for further reasoning or BigQuery ingestion.

Feed exact HUD Census crosswalk data into your Gemini agents using Google ADK.

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Connect HUD User (USPS Crosswalk) MCP to Google ADK

Create your Vinkius account to connect HUD User (USPS Crosswalk) to Google ADK 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 HUD User (USPS Crosswalk) with Google ADK

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Connect the HUD User MCP Server to BigQuery pipelines

Enterprise housing data usually lives in BigQuery, but it often lacks the spatial crosswalks needed to link postal codes to census tracts. You need a way to bridge that gap without writing custom ETL jobs. This MCP Server gives your Gemini agent direct access to `zip_to_tract` and `tract_to_zip`. The agent pulls the HUD allocation ratios on the fly and immediately uses them to enrich your existing BigQuery datasets.

Map broad statistical areas to local mail routes

When analyzing regional economic trends, you work with Core Based Statistical Areas. Translating those massive CBSAs down to actionable ZIP codes takes significant manual effort. Your agent uses `cbsa_to_zip` and `cbsadiv_to_zip` to break down large metropolitan areas. Gemini's massive context window can hold the entire list of returned ZIP codes while cross-referencing it against your internal Vertex AI models.

Translate congressional districts for policy agents

Policy analysis requires mapping voter districts to physical mailing addresses. ZIP codes cross district lines constantly, making standard database joins highly inaccurate. By calling `zip_to_cd` and `cd_to_zip`, your agent fetches the exact federal crosswalk data. It sees exactly how a postal route splits across multiple congressional districts based on HUD's quarterly updates.

Setup guide

Set up HUD User (USPS Crosswalk) MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with HUD User (USPS Crosswalk) tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="HUD User (USPS Crosswalk)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to HUD User (USPS Crosswalk) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about HUD User (USPS Crosswalk) MCP in Google ADK

Install google-adk and initialize an McpToolset with your Vinkius HTTP endpoint. Pass this toolset directly to your LlmAgent to expose the geographic mapping tools.

You can use the tool_names filter in the ADK to limit exposure. If your agent only needs county-level data, you restrict it to just `zip_to_county` and `county_to_zip`.

Static tables get outdated fast. The MCP server queries the live HUD API, ensuring your Gemini agent always uses the most recent postal delivery route adjustments.

Yes. When a tool like `county_to_zip` returns hundreds of postal codes, Gemini easily holds the entire JSON response in context for further reasoning or BigQuery ingestion.

The server receives the ZIP code, queries the HUD API, and returns the geographic mapping. Vinkius runs this in an ephemeral V8 isolate that retains zero memory of your spatial queries once the execution finishes.

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