content_ai-score
shallowcom.tollmint/gateway · Verify this server
Measure how mechanically a piece of text reads across 19 stylistic signals: sentence-length variance (burstiness), em-dash rate, negative parallelism, copula avoidance, the characteristic AI vocabulary cluster, hedged superlatives, significance inflation, puffery, vague attribution, over-signposting, formatting tics, and more. Returns a per-sentence breakdown naming which signal each one tripped. Deterministic and model-free, so before-and-after comparisons across an edit are meaningful. NOT an AI detector — it reports stylistic properties, never authorship, and returns no verdict or probability. Authorship classifiers are unreliable and disproportionately misjudge non-native English writers. Costs $0.004000 per call.
1 trials · measured 8 days ago
content_ai-score scores 100.0/100 on Vouch's measured behaviour index, from 1 real invocation trials against com.tollmint/gateway, 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
- Category
- Communication
- 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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