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⌁ mcp.call("adw-302") v1.0 How healthy or at-risk is this US ZIP code, and what's driving it?
📈 Live history is activating for this object — the automated backfill is in progress.
⌁ mcp.call("adw-302") v1.0 The widget below calls the live ADW-302 endpoint in real time. Enter any 5-digit US ZIP to pull a health-risk score, confidence, and top drivers — the same call your agent makes.
Sample · ZCTA preview
Try your ZIP →
Real-time score from the ADW API
National context
Top risk drivers · sample
Enter a ZIP above to see real drivers for that area.
Lookup failed
Check the ZIP and try again, or try one of the sample codes above.
National context
Top risk drivers
Each layer is tracked independently, giving agents a time-series signal per health domain — not just a single composite.
/v1/geohealth/{entity}/history
— pass any ZIP, county FIPS, or county name (e.g. 17031 / Cook County, IL).
Per measure: national percentile rank across 32,520 ZCTAs, polarity-adjusted (higher=worse), weighted 1-3 (mortality-linked chronic disease=3) -> 0-100 composite; confidence = fraction of 40 measures present (small ZCTAs lower).
Version 1.0 · validated to beat a naive baseline · benchmark: none packaged (CDC PLACES is raw tables; this is the assembled agent-ready index)
One call returns the answer with its reasoning attached — the live Intelligence Object for ADW-302.
{} Every product conforms to the Intelligence Object Model — typed, versioned, and discoverable.
Dashboard
Read the score + drivers in the console.
REST API
/v1/geohealth/{zip}
MCP tool
adw.health_risk
Marketplace
Discoverable by any MCP agent via the MCP registry.
White-label
Embed under your own brand (Platinum).
Enable healthcare administrators to proactively recruit and deploy nursing staff before critical staffing shortages impact patient care and operational costs.
Method: Aggregate real-time admission rates, historical seasonal trends, and local unemployment data to generate a predictive shortage risk score for specific units; apply time-series forecasting to identify
Enables employers and insurers to proactively deploy mental health interventions and telehealth resources to employees in regions experiencing seasonal light deprivation.
Method: Aggregates local solar irradiance and daylight duration data to calculate a seasonal deficit score, which is then mapped against historical mental health utilization trends to predict risk levels.
How healthy is the US population?
Method: composite z-score 0-100