What is County Business Patterns (CBP)?

CBP is an annual series that provides subnational economic data by industry. This server uses it to return total business establishments, employees, and annual payroll by county.

Does income include taxes?

Median household income reported by the Census represents pre-tax (gross) income from all sources.

Can it separate individual vs household income?

The primary metrics fetched here are Household metrics as they give the highest fidelity snapshot of local living conditions.

U.S. Census Income — Median Income, Poverty & Economy MCP Server for Pydantic AI 4 tools — connect in under 2 minutes

Name: U.S. Census Income — Median Income, Poverty & Economy
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 4 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect U.S. Census Income — Median Income, Poverty & Economy through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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Open in ChatGPT Open in Claude Open in Perplexity

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 U.S. Census Income — Median Income, Poverty & Economy "
            "(4 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in U.S. Census Income — Median Income, Poverty & Economy?"
    )
    print(result.data)

asyncio.run(main())

U.S. Census Income — Median Income, Poverty & Economy

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About U.S. Census Income — Median Income, Poverty & Economy MCP Server

U.S. Census economic data.

Pydantic AI validates every U.S. Census Income — Median Income, Poverty & Economy tool response against typed schemas, catching data inconsistencies at build time. Connect 4 tools through 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.

4 Tools

Income by State — Median income & poverty rates
Income by County — Drill down into local economies
Education by State — Bachelor's degree attainment
Business Patterns — County-level business activity

Authentication

Requires a free API key from the Census Bureau.

The U.S. Census Income — Median Income, Poverty & Economy MCP Server exposes 4 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 U.S. Census Income — Median Income, Poverty & Economy to Pydantic AI via MCP

Follow these steps to integrate the U.S. Census Income — Median Income, Poverty & Economy 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 4 tools from U.S. Census Income — Median Income, Poverty & Economy with type-safe schemas

Why Use Pydantic AI with the U.S. Census Income — Median Income, Poverty & Economy MCP Server

Pydantic AI provides unique advantages when paired with U.S. Census Income — Median Income, Poverty & Economy 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 U.S. Census Income — Median Income, Poverty & Economy integration code

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

Dependency injection system cleanly separates your U.S. Census Income — Median Income, Poverty & Economy connection logic from agent behavior for testable, maintainable code

U.S. Census Income — Median Income, Poverty & Economy + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the U.S. Census Income — Median Income, Poverty & Economy MCP Server delivers measurable value.

Type-safe data pipelines: query U.S. Census Income — Median Income, Poverty & Economy with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple U.S. Census Income — Median Income, Poverty & Economy tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query U.S. Census Income — Median Income, Poverty & Economy and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock U.S. Census Income — Median Income, Poverty & Economy responses and write comprehensive agent tests

U.S. Census Income — Median Income, Poverty & Economy MCP Tools for Pydantic AI (4)

These 4 tools become available when you connect U.S. Census Income — Median Income, Poverty & Economy to Pydantic AI via MCP:

get_business_patterns

From County Business Patterns (CBP) — the definitive dataset for understanding local economic activity and business concentration. Get County Business Patterns — establishments, employees, and payroll by county

get_education_by_state

Education level is a key predictor of income, employment, and economic development. Get educational attainment for all states — bachelor's degree or higher

get_income_by_county

Critical for real estate analysis, business site selection, and understanding economic disparity within a state. Get median household income and poverty for all counties in a state

get_income_by_state

Median income is the single most-used economic indicator from the Census — it determines federal funding, cost-of-living adjustments, and market opportunity analysis. Get median household income and poverty rates for all states

Example Prompts for U.S. Census Income — Median Income, Poverty & Economy in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with U.S. Census Income — Median Income, Poverty & Economy immediately.

"What is the median household income in New York state?"

"Compare poverty levels down the counties of Illinois"

"Show the business patterns for restaurants in Texas"

Troubleshooting U.S. Census Income — Median Income, Poverty & Economy MCP Server with Pydantic AI

Common issues when connecting U.S. Census Income — Median Income, Poverty & Economy to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

U.S. Census Income — Median Income, Poverty & Economy + Pydantic AI FAQ

Common questions about integrating U.S. Census Income — Median Income, Poverty & Economy 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 U.S. Census Income — Median Income, Poverty & Economy MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.