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Queue Consumers Analytics — String Normalisation + Secondary-Measure Weighted Mean
x402
Queue Consumers Analytics — String Normalisation + Secondary-Measure Weighted Mean is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-23).
Performs string normalisation and secondary-measure weighted mean calculations on up to 200 queue consumer records (queue_depth, processed_messages) and returns 2 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Queue consumers — String normalisation + Secondary-measure weighted mean: String normalisation + Secondary-measure weighted mean for supplied queue consumers records (queue_depth, processed_messages). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns 2 independent analytical reports: (1) a string normalisation report cleaning and standardising string fields across the supplied queue consumer rows, and (2) a secondary-measure weighted mean report computing weighted averages over the specified numeric field (queue_depth or processed_messages), all computed locally without external lookups.
Example use cases
Queue depth normalisation for microservices
“I have 50 rows of queue consumer data from my microservices — each row has a component name, date, queue_depth, and processed_messages. Can you normalise the string fields and compute a weighted mean on queue_depth so I can compare components fairly?”
Processed messages weighted mean analysis
“Run string normalisation and a secondary-measure weighted mean on my queue consumer dataset — 120 rows covering various components from 2026-09-01 to 2026-09-30, and focus the weighted mean calculation on processed_messages.”
Daily queue monitoring report generation
“I need two analytical reports on my queue consumer logs: string normalisation for the component names and a weighted mean for queue_depth. Here are 80 rows with row_id, component, date, queue_depth, and processed_messages — can you process them?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-software-07-034?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular queue consumer data (up to 200 rows) and need both string normalisation of component/label fields AND a weighted mean computation on a secondary numeric measure in a single $0.01 call. Ideal for offline/batch analytical processing of queue monitoring data where no live lookup, prediction, or external data enrichment is required.
Side effects
- More than 200 rows submitted — payload rejected due to maxItems constraint
- Missing required fields (row_id, component, date, queue_depth, or processed_messages) — validation error
- Invalid date format (not YYYY-MM-DD) — pattern validation failure
- Numeric values outside allowed range (±1,000,000,000,000) — schema rejection
- Empty rows array — minItems violation
- Invalid field enum value (not queue_depth or processed_messages) — schema error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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