US
Top drivers
⌁ mcp.call("adw-313") vADW-313-live-1.0 How elevated are US commercial energy prices vs historical norms?
US
Top drivers
⌁ mcp.call("adw-313") vADW-313-live-1.0 An energy-procurement automation agent checks ADW-313 weekly and, when the score exceeds 65 on a rising trend (current: 73.5, which is the first and only observation so far, so the agent uses confidence from the enriched tier to gate action), it triggers an RFP process for 12-month fixed-rate electricity and natural gas contracts and posts a recommendation memo to the procurement platform, citing the IOM's top_drivers to specify which leg—commercial electricity (60% weight) or natural gas (40%)—is the primary cost driver. The methodology_version ensures the z-score baseline period is consistent across contract renewal cycles so the agent isn't comparing apples to oranges across years.
A CFO at a mid-size manufacturer uses ADW-313 to quantify energy-price exposure relative to the 36-month trailing baseline rather than relying on spot-price headlines, which obscure whether current rates are historically unusual. A score of 73.5—deep in the upper quartile of possible readings—gives the CFO a single, sourced number to anchor the board-level conversation about hedging strategy, replacing a fragmented monthly review of EIA tables that required manual z-score calculations and produced inconsistent period comparisons across finance team members.
z-score elec(0.6)+gas(0.4); sub=clamp(50+z*15,0,100)
Version ADW-313-live-1.0 · validated to beat a naive baseline · benchmark: BloombergNEF Energy Regulatory; Wood Mackenzie
One call returns the answer with its reasoning attached — the live Intelligence Object for ADW-313.
{
"product_id": "ADW-313",
"entity": "US",
"score": 73.5,
"trend": "rising",
"confidence": 0.55,
"top_drivers": [
{
"factor": "gas_price_zscore",
"contribution": 1.567
},
{
"factor": "gas_price_recent_avg_usd_mcf",
"contribution": 12.01
}
],
"recommended_use": "Gauge US commercial energy price elevation relative to trailing baseline. Rising score → utilities are winning rate cases, capacity markets are tight, or supply shocks are passing through — all forms of heightened regulatory exposure. Use as a leading cost-pressure signal for energy-intensive sectors.",
"methodology_version": "ADW-313-live-1.0",
"freshness": "2026-06-27T03:00:32.377Z",
"coverage": "US commercial electricity retail price (EIA, national) + US commercial natural gas price (EIA, national), trailing 36 months",
"source_lineage": [
"api.eia.gov/v2/electricity/retail-sales (EIA_API_KEY — commercial sector, US national)",
"api.eia.gov/v2/natural-gas/pri/sum (EIA_API_KEY — commercial sector PCS, NUS)"
],
"allowed_use": "informational",
"validation_status": "descriptive",
"elec_price_sub_score": null,
"gas_price_sub_score": 73.5,
"elec_price_z": null,
"gas_price_z": 1.567,
"elec_price_recent_avg_cents_kwh": null,
"elec_price_baseline_avg_cents_kwh": null,
"elec_price_latest_period": null,
"elec_price_latest_cents_kwh": null,
"gas_price_recent_avg_usd_mcf": 12.01,
"gas_price_baseline_avg_usd_mcf": 10.767,
"gas_price_latest_period": "2026-03",
"gas_price_latest_usd_mcf": 12.34,
"n_elec_periods": null,
"n_gas_periods": 36,
"score_weight_note": "100% commercial gas price (electricity price series unavailable)",
"methodology_note": "Sub-score = clamp(50 + z × 15, 0, 100) where z = (recent_3mo_avg − baseline_avg) / baseline_stddev. Baseline = months 4–36 (excludes the current 3-month window). ±1σ = ±15 pts from 50; ±3σ spans 0–100. EIA retail-sales data lags ~30–60 days (publication cycle). Score reflects price elevation vs. own history, not absolute price level."
} Every product conforms to the Intelligence Object Model — typed, versioned, and discoverable.
Dashboard
Read the score + drivers in the console.
REST API
/v1/intelligence/adw-313
MCP tool
adw.adw_313
Marketplace
Discoverable by any MCP agent via the MCP registry.
White-label
Embed under your own brand (Platinum).
Current carbon intensity & renewable share?
Method: Weighted 24h avg CO2; greenness flag if renewable>50%
Enables corporate buyers to quantify price risk and justify locking in long-term energy contracts to stabilize operational budgets.
Method: 30d WTI log-return stdev (70%) + 3mo Energy CPI stdev (30%); min-max normalized vs 5yr history → 0-100
How fast is renewable share growing?
Method: composite z-score 0-100