Census Bureau

@census-bureau what was the median household income in Travis County, Texas according to the most recent ACS 5-year estimates?

Based on the 2022 ACS 5-Year Estimates, Travis County, Texas (FIPS code 48453) had a median household income of $82,043. This represents the midpoint of all household incomes in the county, with half of households earning more and half earning less. The estimate has a margin of error of ±$1,876 at a 90% confidence level, which is typical for ACS survey data. This figure places Travis County above the Texas state median of $67,321 for the same period.

This example demonstrates straightforward demographic data retrieval. The MCP requires an API key from data.census.gov. Users should understand that ACS estimates include margins of error because they're based on survey samples, not complete counts. Geography can be specified by name or FIPS code—the MCP handles both.

@census-bureau compare population growth rates for all counties in Arizona between 2020 and 2023 using the Population Estimates Program.

Here's the population change analysis for Arizona counties from 2020-2023 using PEP data: Maricopa County led with 4.8% growth (4.51M to 4.73M residents), followed by Pinal County at 4.2% growth. Yavapai and Coconino counties grew 3.1% and 2.7% respectively. Apache and Greenlee counties showed minimal growth under 1%. La Paz County experienced a slight decline of -0.3%. Statewide, Arizona grew 3.6% during this period, outpacing the national average of 2.1%. This data reflects post-census estimates updated annually through the PEP program.

This showcases the MCP's ability to retrieve and compare data across multiple geographies within a state. The AI synthesizes growth rates from raw population counts. Note that PEP estimates are updated annually and become more reliable as they incorporate additional administrative records—early post-census years may see revisions.

@census-bureau pull county business patterns data for NAICS code 722511 (full-service restaurants) in King County, Washington for 2021, then summarize the employment distribution by establishment size.

For full-service restaurants (NAICS 722511) in King County, WA in 2021, County Business Patterns shows 1,847 establishments employing 38,624 people total. Employment distribution: 42% worked at establishments with 20-49 employees (the modal size class), 28% at establishments with 50-99 employees, 18% at smaller operations with 10-19 employees, and 12% at locations with 100+ employees. The data shows 156 establishments with payroll over $1M annually. This pattern reflects King County's mix of independent restaurants and regional chains, with mid-sized operations dominating the market.

This example combines data retrieval with AI analysis to extract business insights. CBP data is powerful for industry research but has important limitations: it excludes non-employer businesses (sole proprietors without employees) and uses establishment counts, not company counts—a chain with 50 locations appears as 50 establishments. Data is typically released 18-24 months after the reference year.

When Census data beats third-party demographics for TAM modeling

A 6-person growth team at a vertical SaaS company needs to model total addressable market for three new metro expansions. They pull ACS 5-Year Estimates filtered by industry codes and household income brackets, then cross-reference County Business Patterns to count establishments in their target verticals. The MCP returns structured JSON they pipe directly into their financial model. This works because Census data is free, authoritative, and granular to the census tract level. The trade-off: if your product targets niche demographics not captured in standard ACS variables, you'll need supplemental data sources. For TAM work where standard demographics and business counts matter, this MCP eliminates the Statista subscription and gives you reproducible queries your finance team can audit.

How community orgs use Census MCP to justify funding requests

A 3-person nonprofit applies for state housing grants every quarter and needs current population estimates broken down by age, income, and housing tenure for their service area. They use the Population Estimates and ACS 1-Year tools to pull county-level data, then cite the Variable Details endpoint in their methodology appendix to show funders exactly which Census definitions they used. The MCP saves 4 hours per application compared to manually downloading CSV files and reconciling variable codes. This is the right call if your grant cycle is predictable and your geographies are stable—Census releases data on fixed schedules, so you won't get mid-year updates. For orgs writing 6+ grant applications a year with demographic justification requirements, this MCP turns data gathering from a research project into a 20-minute Switchy prompt.

When to use Census MCP for store location analysis

A 2-person real estate team evaluating 12 potential franchise locations needs foot-traffic proxies: daytime population, median household income, and business density within a 3-mile radius. They query Decennial Census for base population, ACS 5-Year for income distributions, and County Business Patterns for competitor counts, then export the results to a spreadsheet model their franchisor requires. The MCP handles the API pagination and variable lookups they'd otherwise script manually. This breaks down if you need real-time data or non-Census variables like crime rates or school ratings—Census updates are annual at best, and some datasets lag two years. For franchise teams running 10+ site analyses per year where demographics and business counts drive 60% of the scoring model, this MCP is faster than hiring a GIS consultant.