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Survey Participation Data Quality Suite: Record Audit + String Normalisation + Dataset Comparison
x402
Survey Participation Data Quality Suite: Record Audit + String Normalisation + 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 2026-11-04).
Runs three analytical reports on survey participation records (respondents and invitations): a record quality audit, string normalisation, and keyed dataset comparison between two supplied datasets.
Price / call$0.01
Success—
StatusUnknown
How this service works
Survey participation — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison for supplied survey participation records (respondents, invitations). 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 identifying missing, null, or out-of-range values in respondent/invitation fields; (2) string normalisation results for course group labels; and (3) a keyed dataset comparison highlighting rows that differ between the primary and comparison datasets, all computed locally without external lookups.
Example use cases
Pre-analysis survey data validation
“Before I run my analysis, can you audit the quality of these survey participation records for our autumn 2026 courses? I have up to 150 rows with respondent and invitation counts per course group — check for missing values, bad formats, and normalise the course group strings.”
Detecting dataset version discrepancies
“I have two exports of our survey participation data — one from last week and one from today — both keyed by row ID with respondent and invitation counts per course group. Can you compare them and tell me which rows changed?”
Education survey data clean-up
“Our survey team submitted participation data for course groups Alpha, alpha, and ALPHA across different dates — can you normalise those strings and flag any quality issues in the respondent and invitation numbers across these 80 rows?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-education-10-207?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three simultaneous data quality operations on survey participation records in a single $0.01 call: a quality audit, string normalisation, and a keyed comparison between two dataset versions. It is ideal for education-domain survey data with course group segmentation and respondent/invitation counts, up to 200 rows, where no external lookups or live data sources are needed.
Side effects
- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, course_group, date, respondents, invitations) — validation error
- Malformed date strings not matching YYYY-MM-DD pattern — schema validation failure
- Both 'rows' and 'other' arrays required for comparison report — incomplete input
- Numeric values outside allowed range — validation error
- Payment of 0.01 USDC not completed — 402 Payment Required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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