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Question Bank Coverage — String Normalisation + Secondary-Measure Weighted Mean
x402
Question Bank Coverage — 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 2026-10-30).
Normalises course_group strings and computes a secondary-measure weighted mean over question bank coverage records (item_count, objective_count) for up to 200 rows, returning two analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Question bank coverage — String normalisation + Secondary-measure weighted mean: String normalisation + Secondary-measure weighted mean for supplied question bank coverage records (item_count, objective_count). 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 two analytical reports: (1) normalised string representations of course_group values and (2) a secondary-measure weighted mean computed over the selected field (item_count or objective_count) across all supplied rows (up to 200). All processing is local with no external lookups.
Example use cases
Curriculum coverage balance check
“I have question bank data for two course groups across 10 dates — can you normalise the group labels and give me a weighted mean of objective_count so I can see which curriculum area is most covered? Here are my 10 rows with row IDs, dates, item counts, and objective counts.”
Exam item distribution analysis
“Run a weighted mean analysis on item_count for these 50 question bank records from our assessment team — the field is item_count, and I need the string normalisation applied to course_group names too since different authors wrote them inconsistently.”
Education data quality and summary report
“We've got 150 rows of question bank coverage data with course_group, date, item_count, and objective_count. Can you normalise the course group strings and compute the secondary-measure weighted mean using objective_count as the field, and give me both analytical reports?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-education-18-034?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 question bank coverage records and need both string normalisation of course group labels and a secondary-measure weighted mean computed locally without any external data lookup, prediction, or professional assessment. It is particularly suited to education analytics pipelines processing assessment item inventories.
Side effects
- More than 200 rows submitted — endpoint rejects with validation error
- Missing required fields (row_id, course_group, date, item_count, or objective_count) cause schema validation failure
- Invalid date format (not YYYY-MM-DD) triggers pattern mismatch error
- Null values for both numeric fields may produce undefined weighted mean
- field enum value outside 'item_count' or 'objective_count' causes rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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