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Tutoring Sessions — Record Quality Audit + String Normalisation + Keyed Dataset Comparison
x402
Tutoring Sessions — Record Quality Audit + String Normalisation + Keyed Dataset Comparison is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-25).
Runs three local analytical reports on tutoring session records: a record quality audit, string normalisation, and keyed comparison between two datasets — for up to 200 rows per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Tutoring sessions — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison for supplied tutoring sessions records (session_minutes, session_count). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Returns three independent reports: (1) a record quality audit flagging nulls, out-of-range values, and format issues in session_minutes and session_count fields; (2) a string normalisation report for course_group and other text fields; and (3) a keyed dataset comparison report showing row-level differences between the primary and secondary datasets matched by row_id.
Example use cases
Monthly tutoring data quality check
“I have this month's tutoring session log with course groups, dates, session_minutes, and session_count for about 150 rows. Can you run a full record quality audit on it and also normalise any inconsistent course group strings?”
Compare two versions of session export
“I exported our tutoring session data from two different systems and I want to see where they disagree. Can you do a keyed comparison between these two datasets — both have the same row_ids, course_group, date, session_minutes, and session_count fields?”
Validate session records before import
“Before I import these 80 tutoring session rows into our database, can you check them for data quality issues — things like nulls, bad date formats, or weird values in session_minutes and session_count — and also normalise the course group labels?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-education-16-207?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to validate, clean, and compare structured tutoring session tabular data without any live lookups or external data sources. It is ideal for offline data pipeline quality gates, pre-import validation, or reconciling two exports of the same tutoring dataset. It is not suitable for predictions, live data fetching, or datasets larger than 200 rows.
Side effects
- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, date, course_group, session_minutes, session_count) — validation error
- Malformed date format (not YYYY-MM-DD) — schema rejection
- Numeric values outside allowed range (±1,000,000,000,000) — validation error
- Empty rows array — minimum items constraint violation
- Mismatched row_ids between primary and comparison dataset — keyed comparison may yield all-unmatched results
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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