Government data infrastructure · Census first

The Census layer developers should have had.

PrairieCloud turns Census Bureau tables, FIPS geography, boundaries, and margins of error into a clean API surface for products that need trustworthy public data.

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Free tier. No credit card. 1,000 requests/month.

901M+

ACS observations

392

curated variables

geography levels

ACS vintages

prairiecloud.py

import requests

response = requests.get(
    "https://api.prairiecloud.io/v1/data",
    params={
        "variables": "pop_total,income_median_household",
        "geo": "county:Travis County, Texas",
        "vintage": 2024,
        "include_geometry": "true"
    },
    headers={"X-API-Key": "pck_live_your_key_here"}
)

county = response.json()["data"]["county:48453"]
print(county["name"])
print(county["variables"]["pop_total"]["estimate"])
# Travis County, Texas
# 1290188.0

Working example

Texas population change, county comparison to tract drill-down.

See a real PrairieCloud workflow using ACS 2019 and 2024 data, readable variables, and name-based geography.

View the example

Product posture

Not a wrapper. A cleaner data contract.

The Census API exposes technically available data. PrairieCloud focuses on the part teams actually pay for: normalization, naming, geography handling, and reliable developer ergonomics.

Names instead of archaeology

Use pop_total and county:Travis County, Texas. Keep Census codes as aliases, not as your product interface.

Geography is part of the response

Ask for demographic values and boundary geometry together. No separate shapefile hunt before you can draw a map.

Trends survive boundary changes

Handle tract and block group boundary changes when longitudinal work requires it.

Before / after

Census data, without the Census API ceremony.

Replace obscure table codes, nested geography syntax, and array parsing with named fields and structured JSON.

before.py

import requests

rows = requests.get(
    "https://api.census.gov/data/2022/acs/acs5",
    params={
        "get": "B01003_001E,NAME",
        "for": "county:453",
        "in": "state:48"
    }
).json()
headers, values = rows[0], rows[1]
population = int(dict(zip(headers, values))["B01003_001E"])

Use cases

Built around real Census workflows.

SVI / ADI / EJScreen

Public health and epidemiology

Compute neighborhood indices at the geography level where the methodology actually lives.

CRA / QCT / CDFI

Lending and community development

Resolve tract-level eligibility and market questions without maintaining FIPS lookup tables.

GeoJSON / ACS / choropleths

Civic tech and GIS products

Return boundaries with data and move directly from query to map layer.

MCP / agents / workflows

AI and automation teams

Give LLM tools controlled access to Census data without scraping or bespoke adapter code.

Method

From public tables to product-grade data.

PrairieCloud is designed as infrastructure, not a demo. The interface stays boring where it should: predictable auth, explicit quotas, typed fields, and repeatable responses.

Request

Human-readable variable and geography names.

Resolve

PrairieCloud maps aliases, FIPS, wildcards, and parent/child geography.

Normalize

Schemas, margins of error, boundaries, and vintages are returned consistently.

Deliver

REST JSON for applications or MCP tools for agents.

Capabilities

Geography-aware by default.

The useful parts of Census work are rarely just a single value. PrairieCloud keeps names, geometry, vintages, and AI access in the same product surface.

Smart geography

Ask for the place the way humans describe it.

Pass county names, metro names, or parent geographies and let PrairieCloud resolve the underlying geography set.

smart-geo.py

# Every tract in the Dallas-Fort Worth metro
requests.get(
    "https://api.prairiecloud.io/v1/data",
    params={
        "variables": "pop_total,poverty_below",
        "geo": "tract:Dallas-Fort Worth"
    },
    headers={"X-API-Key": "pck_live_your_key_here"}
)
# Returns 1,900+ tracts across 11 counties

MCP server

Give agents a governed Census data tool.

Eight read-only tools expose data retrieval, comparison, geocoding, variable search, and time series workflows.

agent-workflow

User: "Median household income near 30.27, -97.74?"

Agent: geocode(lat=30.27, lng=-97.74)
       → block_group:484530011032

       get_census_data(
         variables="income_median_household",
         geo="block_group:484530011032"
       )
       → $87,450 ± $8,210

Access

Two interfaces. One data model.

REST API

For applications, dashboards, analysis jobs, and internal data products.

Data, compare, timeseries, variables, geography, topics, datasets
JSON responses with API key auth
Usage dashboard and quota tracking
Python, R, JavaScript, cURL — any HTTP client

View API docs →

MCP Server

For Claude Desktop, Cursor, Windsurf, and MCP-compatible automation.

Data retrieval, comparison, geocoding, and discovery tools
Native coordinate/address → Census geography lookup
Read-only product surface for controlled agent access
Available on Pioneer tier and above

MCP setup guide →

MCP demo

Watch Claude query Census data through PrairieCloud MCP.

Ask for a Census comparison in natural language. PrairieCloud resolves the geography, calls the data API, and returns structured ACS results without manual FIPS lookup or table-code archaeology.

Ask a plain-English Census question in Claude.

Approve the PrairieCloud MCP connector request.

Get ACS population and income comparisons back with source context.

Transcript excerpt

“What is the median household income and total population actual and percentage difference for Dallas County, Texas between 2019 and 2024?”

Claude calls PrairieCloud:get_census_data and returns the county comparison from ACS 5-Year Estimates.

iOS screen recording. Claude interface and connector approval screens may change.

Pricing

Start free. Scale when workflows get heavier.

Every tier includes the full variable catalog, all 35 vintages, and direct lookups at every geography level. Higher tiers unlock wildcard expansion, boundary workflows, and higher limits.