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US Site Selection
x402
US Site Selection is a paid API for AI agents from agent-vending-production.up.railway.app, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-11).
Rank and score U.S. geographic locations (states, counties, places, or ZCTAs) against buyer-defined Census and economic criteria with transparent weighting.
Price / call$0.01
Success—
StatusUnknown
How this service works
Pay-per-call machine utilities for autonomous agents.
Output
A ranked list of up to 25 U.S. geographic locations with composite scores and per-metric breakdowns, filtered by any hard constraints supplied, ordered from best to worst match against the caller-defined criteria and weights.
Example use cases
Manufacturing plant site search
“I need to find the best 10 U.S. counties to build a new manufacturing facility — rank them by QCEW employment growth over 5 years, average annual pay, and labor force size, with a hard filter of at least 25,000 employed people. Limit the search to Midwest states.”
Retail expansion market scoring
“Score all U.S. places with at least 10,000 people on median household income, population growth, and low poverty rate — weight income the most — and give me the top 15 results so I can prioritize where to open new retail stores.”
Remote workforce hub identification
“Find the top 10 U.S. ZIP codes where I should recruit remote workers — rank by bachelor's degree attainment rate, median household income, and homeownership rate, with a minimum population of 5,000, and prefer higher education levels above all else.”
Technical details
Endpoint
POSThttps://agent-vending-production.up.railway.app/v1/site-selection?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need to objectively rank multiple U.S. locations against a custom set of economic, demographic, labor, or housing metrics with user-defined importance weights. It is ideal for site selection, market entry analysis, workforce hub identification, or any decision requiring transparent, criteria-based geographic scoring rather than raw data lookup or simple filtering.
Side effects
- Invalid or unsupported metric name returns a validation error
- Geography type incompatible with selected data source (e.g. census_and_economy requires county) returns an error
- Filters that eliminate all candidates return an empty result set
- top_n exceeding 25 is rejected
- Missing required criteria array may return unranked or error response
- Unsupported state abbreviation or geography scope combination returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| mode | string | |
| top_n | integer | SEARCH mode; default 10 |
| search | object | |
| filters | array | Optional hard filters applied before ranking |
| vintage | string | |
| criteria | array | |
| locations | array | Alias of candidates ("County, ST" or FIPS allowed) |
| candidates | array | |
| data_scope | string | Default "census" |
| target_market | object | Optional (CANDIDATE mode, <= 10 candidates): Market Size household criteria, use "supplemental" (default) or "scoring" |
| target_industry | object | |
| category_weights | object | Optional weight per active category (DEMOGRAPHICS, INCOME, HOUSING, WORKFORCE, EDUCATION, HEALTH_COVERAGE, TARGET_MARKET); normalized to 1 |
| balance_categories | boolean | Default true |
| percentile_universe | string | |
| include_latest_context | boolean | Not used in SiteScore |
| scoring_reference_period | string |
Output
Schema · Response
{
"type": "json",
"example": {
"mode": "CANDIDATE",
"status": "OK",
"results": [
{
"rank": 1,
"metrics": {
"average_annual_pay": {
"value": 77892,
"percentile": 97.09,
"contribution": 0.728,
"reference_period": "2023"
}
},
"geography": {
"name": "Orange County, California",
"geoid": "06059",
"geo_type": "county"
},
"site_score": 59.73,
"close_score": false,
"explanation": [
"Ranked #1 under supplied criteria (SiteScore 59.73 of 100)."
],
"category_scores": {
"WAGES": 2.91,
"REGIONAL_INCOME": 98.03,
"BUSINESS_ACTIVITY": 89.67,
"LOCAL_LABOR_MARKET": 48.3
},
"category_contributions": {
"WAGES": 0.728,
"REGIONAL_INCOME": 24.507,
"BUSINESS_ACTIVITY": 22.4185,
"LOCAL_LABOR_MARKET": 12.0741
}
}
],
"data_scope": "census_and_economy",
"methodology": {
"score_meaning": "Relative fit under the buyer's supplied criteria and weights."
},
"site_selection_version": "2.0",
"scoring_reference_period": 2023
}
}Tags
x402
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