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Point Clouds Data Quality Audit, String Normalisation & Numeric Row Ordering
x402
Point Clouds Data Quality Audit, String Normalisation & Numeric Row Ordering is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-23).
Runs three independent analytical reports on point cloud records: a record quality audit, string normalisation, and numeric row ordering for up to 200 rows of point_count and file_mb data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Point clouds — Record quality audit + String normalisation + Numeric row ordering: Record quality audit + String normalisation + Numeric row ordering for supplied point clouds records (point_count, file_mb). 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 analytical reports: (1) a record quality audit flagging missing, null, or malformed values across rows; (2) string normalisation results for the asset_group field; and (3) rows reordered numerically by the chosen field (point_count or file_mb). All processing is performed locally with no external lookups.
Example use cases
LiDAR survey file inventory audit
“I have a list of 50 LiDAR point cloud files with row IDs, asset groups, dates, point counts, and file sizes in MB. Can you run a full quality audit, normalise the asset group names, and order them by file_mb so I can spot any bad records and get a clean sorted list?”
3D scan data normalisation for pipeline ingestion
“Before I ingest these 3D scan records into our processing pipeline, can you check them for quality issues, standardise the asset_group strings, and sort by point_count? Here are the rows: [{"row_id":"scan-01","asset_group":"Building B","date":"2024-11-01","point_count":1200000,"file_mb":340},{"row_id":"scan-02","asset_group":"building b","date":"2024-11-02","point_count":980000,"file_mb":null}].”
Point cloud batch QA for project handover
“We're handing over a project and need to validate the quality of our point cloud dataset records, normalise the group labels, and sort everything by point_count descending. Can you run those three reports on these 30 rows I'm sending?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-creative-22-196?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to simultaneously audit data quality, normalise string fields, and sort by a numeric column on point cloud records (point_count, file_mb) in a single low-cost call. It is ideal for batch pre-processing of LiDAR or 3D scan metadata before ingestion into a pipeline, and when you want three bundled analytical reports without any external API calls or live data lookups.
Side effects
- Empty rows array returns an error (minItems: 1)
- More than 200 rows submitted exceeds maxItems limit
- Invalid date format (not YYYY-MM-DD) fails pattern validation
- Invalid field enum value (not point_count or file_mb) rejected
- row_id missing or empty string fails minLength validation
- Numeric fields outside allowed range rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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