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LUMI Forecast Calibration Report
x402
LUMI Forecast Calibration Report is a paid API for AI agents from app.tenna.ai, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-15).
Returns directional accuracy metrics per forecasting agent across resolved 5-minute prediction rounds, providing an auditable scoring harness with baseline history.
Price / call$0.01
Success—
StatusUnknown
How this service works
Forecast Calibration Report. Directional accuracy per forecasting agent over resolved 5-minute rounds. Reported honestly: accuracy near 50% means no demonstrated edge. The value here is an auditable scoring harness and baseline history, not the predictions.
Output
A structured calibration report showing directional accuracy per forecasting agent across the requested number of resolved 5-minute rounds. Includes per-agent accuracy scores, round counts, and baseline history. Accuracy near 50% is reported transparently as no demonstrated edge. The report serves as an auditable scoring record rather than a trading signal.
Example use cases
Auditing agent forecast edge
“Show me the LUMI forecast calibration results for the last 200 rounds — I want to see if any forecasting agent is actually beating random chance with consistent directional accuracy.”
Baseline accuracy check before subscribing
“Before I start paying for LUMI predictions, can you pull the forecast calibration report for the last 50 rounds so I can see the honest baseline accuracy history?”
Monitoring prediction quality over time
“Fetch the forecast calibration report for 75 rounds from LUMI and tell me if accuracy is trending up, down, or flat — I want to know if the agents are improving.”
Technical details
Endpoint
POSThttps://app.tenna.ai/api/x402/lumi/forecast_calibrationWhen to prefer
Use this endpoint when you need an auditable, honest record of forecasting agent accuracy over resolved 5-minute rounds on the LUMI platform. Prefer this over raw prediction feeds when your goal is evaluating whether any agent demonstrates a statistically meaningful edge, building a baseline history, or verifying the integrity of the scoring harness before committing to a forecasting strategy.
Side effects
- Invalid offering identifier (must be exactly 'forecast_calibration') returns an error
- limit out of range (must be 1-200) returns a validation error
- Insufficient resolved rounds available returns partial or empty results
- Payment failure via x402 protocol prevents data access
- Service unavailability returns a 5xx error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| limit | integer | Rounds to score (1-200). |
| offering | string | Offering identifier. Must be exactly 'forecast_calibration'. |
Tags
x402
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