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Elevation Samples — Record Quality Audit, String Normalisation & Keyed Dataset Comparison
x402
Elevation Samples — 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 2026-10-22).
Runs three local analytical reports on elevation sample records: a record quality audit, string normalisation, and a keyed comparison between two supplied datasets — no external lookups or live data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Elevation samples — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison for supplied elevation samples records (elevation_m, sample_weight). 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
Three independent analytical reports: (1) a record quality audit flagging missing, out-of-range, or malformed values across all fields; (2) a string normalisation report identifying and correcting inconsistencies in text fields like station_group; and (3) a keyed dataset comparison highlighting row-level differences in elevation_m and sample_weight between the two supplied datasets. All processing is local with no external data sources.
Example use cases
Pre-ingestion elevation data quality check
“Before I load these weather station elevation readings into our database, can you run a record quality audit and string normalisation on them? I've got about 150 rows with row_id, station_group, date, elevation_m, and sample_weight fields and want to catch any bad values or inconsistent strings first.”
Detecting drift between two dataset versions
“I have two versions of our elevation sample dataset — the original and an updated one — each with the same row IDs. Can you do a keyed comparison between them to find which rows changed in elevation_m or sample_weight, alongside a quality audit and string normalisation on both?”
Validating merged station group records
“We just merged elevation data from two station groups into one dataset and I need to check for string inconsistencies in the station_group field, flag any quality issues in the elevation_m and sample_weight columns, and compare the merged set against our reference dataset by row key. Can you run all three reports on up to 200 rows?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-13-207?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need simultaneous data quality auditing, string normalisation, and cross-dataset comparison for elevation sample records in a single low-cost call. It is ideal for pre-ingestion validation pipelines, versioned dataset drift detection, or QA workflows on weather station elevation data where no external lookups or ML predictions are needed. Best suited for datasets of up to 200 rows where all three report types are needed together.
Side effects
- Input array exceeds 200 rows — request will be rejected
- Missing required fields (row_id, station_group, date, elevation_m, sample_weight) on any row — validation error
- Malformed date strings not matching YYYY-MM-DD pattern — schema validation failure
- row_id values exceeding 80 characters — schema rejection
- Payment of 0.01 USDC not completed — 402 response blocking execution
- Both 'rows' and 'other' arrays must be provided for the keyed comparison report
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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