get_attention_metrics
shallowio.github.snehdhruv/trillboards-dooh · Verify this server
Get edge AI attention metrics for a campaign (FEIN-powered). This is what makes DOOH attribution better than digital: Trillboards MEASURES viewability via FEIN edge AI instead of estimating it. WHEN TO USE: - Measuring actual human attention to ads (not just impressions) - Comparing attention-adjusted CPM (aCPM) vs standard CPM - Getting face count, dwell time, and emotion engagement data RETURNS: - impressions: total, uniqueDevices - attention: avgScore (0-1), medianScore, p90Score, avgDwellSeconds, avgFaceCount, qualifiedPct - economics: standardCpm, attentionCpm (aCPM), costPerAttentiveReach - emotion: avgEngagement (0-1), positiveEmotionPct aCPM = total_media_cost / (SUM(attention_score * face_count) / 1000)
1 trials · measured 8 days ago
get_attention_metrics scores 100.0/100 on Vouch's measured behaviour index, from 1 real invocation trials against io.github.snehdhruv/trillboards-dooh, measured 25 Aug 2026 under methodology v0.2.0. Every measured component scored 100.
Component breakdown
| Component | Weight | Value |
|---|---|---|
| Reliability | 35% | not applicable |
| Schema integrity | 25% | 100.0 |
| Failure behaviour | 15% | not applicable |
| Latency | 15% | not applicable |
| Concurrency | 10% | not applicable |
Tool details
- Transport
- remote
- Credential class
- self-provisionable
- Input schema
- not declared
- Output schema
- not declared
- Side-effect classification
- unclassified
Score history
| Day | Score | Tier | Methodology |
|---|---|---|---|
| 2026-08-25 | 100.0 | shallow | v0.2.0 |
Probe evidence
| Probe | Outcomes |
|---|---|
| schema_integrity | pass: 1 |
Raw request/response logs are not archived yet — the outcome counts above are drawn directly from every recorded trial.
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[](https://vouch.tools/tools/59fc5532-8222-40da-8a1e-46971fe06c0e)