How to Use the U.S. Census Income — Median Income, Poverty & Economy MCP in Google ADK

Q: Can Google ADK access county-level U.S. Census Income — Median Income, Poverty & Economy metrics?

Yes. The agent uses getincomebycounty to pull detailed median household income and poverty information for any specific county within a state.

Q: Does the Google ADK support using U.S. Census Income — Median Income, Poverty & Economy for business pattern analysis?

It does. By calling getbusinesspatterns, your agent can cross-reference location data with economic indicators like payroll and establishments.

Integrate U.S. Census data into enterprise workflows with Google ADK.

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Connect U.S. Census Income — Median Income, Poverty & Economy MCP to Google ADK

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Setup U.S. Census Income — Median Income, Poverty & Economy with Google ADK

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Identify local economic activity via CBP.

The `get_business_patterns` tool reads the County Business Patterns (CBP) dataset for you. It provides counts of establishments, employees, and payroll dollars by county. This is essential when mapping out where business growth is actually happening.

Get educational attainment across states.

Need to factor education into your Google Cloud models? `get_education_by_state` fetches educational attainment (bachelor's degree or higher) for all U.S. states. You can feed this structured data directly into your long-context Gemini reasoning.

Model income and poverty by county.

Using `get_income_by_county`, you get median household income and poverty rates for every county in a state. This supports sophisticated site selection logic that needs granular, localized economic data points.

Perform state-wide income analysis.

For macro trends, use `get_income_by_state`. It delivers median household income and poverty rates for every state. Your agent can then compare these figures against other BigQuery datasets you already hold.

Setup guide

Set up U.S. Census Income — Median Income, Poverty & Economy 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 U.S. Census Income — Median Income, Poverty & Economy 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="U.S. Census Income — Median Income, Poverty & Economy_agent",
    model="gemini-2.0-flash",
    instruction="You have access to U.S. Census Income — Median Income, Poverty & Economy tools via MCP.",
    tools=mcp_tools,
)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by U.S. Census Bureau. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about U.S. Census Income — Median Income, Poverty & Economy MCP in Google ADK

You pass the MCP Server as a toolset to your LlmAgent. When you ask for median income, the agent executes the necessary calls—like `get_income_by_state`—and returns clean data ready for BigQuery storage.

Yes. The agent uses `get_income_by_county` to pull detailed median household income and poverty information for any specific county within a state.

The long-context reasoning of Gemini models handles the complexity. It holds the entire conversation and multiple tool results (e.g., CBP + income data) simultaneously for final analysis.

The server only accesses aggregated census and economic statistics, including median household income and poverty rates. No private or personally identifiable information is involved.

It does. By calling `get_business_patterns`, your agent can cross-reference location data with economic indicators like payroll and establishments.

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