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Survey Scores String Normalisation
x402
Survey Scores String Normalisation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-12).
Normalises string representations of survey score records (up to 200 rows) using local numeric and data-quality processing.
Price / call$0.01
Success—
StatusUnknown
How this service works
Survey scores - String normalisation: String normalisation for Survey scores. Up to 200 supplied typed records. Local numeric and data-quality processing; no external enrichment or forecasting. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded string normalisation deliverable.
Output
Returns normalised survey score records with cleaned and standardised string fields for each submitted row, applying local numeric and data-quality processing. No external enrichment is performed. Output corresponds one-to-one with the submitted rows (up to 200).
Example use cases
Pre-analysis survey data cleanup
“I've got 50 survey score rows with messy string formats across two questions — can you normalise all of them so scores and response counts are consistently formatted before I run my analysis? Dates are in YYYY-MM-DD, scores range from 0 to 100.”
Multi-question score standardisation
“We ran a survey with three question groups and collected 120 response records. Can you normalise the score strings across all of them so we have consistent data quality before we build the dashboard?”
QA pass on weekly survey export
“Here are 80 rows from this week's survey export with row IDs, question labels, dates, scores, and response counts — please run a string normalisation pass on them so the data is clean and ready for our reporting pipeline.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-analytics-survey-scores-normalise?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, local string normalisation of survey score records without external API dependencies or enrichment. It is ideal for pre-processing pipelines where data quality and consistent formatting of score strings are needed before aggregation, reporting, or ML ingestion — particularly for batches of up to 200 typed survey records.
Side effects
- Batch exceeds 200 rows — request rejected with validation error
- Missing required fields (row_id, question, date, score, responses) — row-level validation failure
- Invalid date format (not YYYY-MM-DD) — pattern validation error
- Score or responses value outside allowed numeric range — boundary validation error
- Empty rows array — minimum items constraint violation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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