score_return_risk
shallowio.github.48x-ai/marketbasketanalysis-mcp · Verify this server
Predict return risk for a candidate bundle of 2-6 products. Returns the composite bundle return rate (max of items, since one returned item typically returns the whole bundle), each item's historical return rate, and a low/medium/high risk recommendation. Use this when the user asks 'will this bundle get returned?', 'predict return risk for these items', 'fashion bundle risk', 'is this set risky to ship together?', or when an agent is composing a bundle and wants to verify it won't tank the merchant's return KPIs. Backed by return-aware mining over the merchant's real order + refund history.
1 trials · measured 2 days ago
score_return_risk scores 100.0/100 on Vouch's measured behaviour index, from 1 real invocation trials against io.github.48x-ai/marketbasketanalysis-mcp, measured 31 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 + stdio
- Credential class
- self-provisionable
- Category
- Commerce & retail
- Input schema
- not declared
- Output schema
- not declared
- Side-effect classification
- unclassified
Score history
| Day | Score | Tier | Methodology |
|---|---|---|---|
| 2026-08-31 | 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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