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Mesh Budgets — Record Quality Audit + String Normalisation + Keyed Dataset Comparison
x402
Mesh Budgets — 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 2026-10-27).
Audits mesh budget records (triangle_count, vertex_count) for data quality, normalises string fields, and compares two keyed datasets, returning three independent analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Mesh budgets — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison for supplied mesh budgets records (triangle_count, vertex_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
Three independent analytical reports: (1) a record quality audit highlighting nulls, out-of-range values, and format violations in triangle_count and vertex_count; (2) a string normalisation report for text fields like asset_group; and (3) a keyed dataset comparison report identifying rows that differ, are missing, or were added between the two supplied datasets.
Example use cases
Pre-pipeline mesh data validation
“I have a batch of 3D asset records with triangle_count and vertex_count values — can you run a quality audit on them to catch nulls, out-of-range values, and string inconsistencies in the asset_group field before I load them into the pipeline?”
Comparing two mesh budget exports
“I exported our mesh budget data from two different tools and want to know where they differ — can you compare the two datasets row by row using row_id as the key and flag any mismatches in triangle_count or vertex_count?”
Normalising asset group names across records
“Our mesh records have inconsistent asset_group labels like 'Group A', 'group-a', and 'GROUP A' — can you normalise those strings and give me a report showing what was cleaned up across the 120 rows?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-creative-01-207?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need offline, local analytical processing of 3D mesh budget tabular data — specifically when you want all three of record quality auditing, string normalisation, and dataset diffing in a single $0.01 call without any external lookups, predictions, or live data dependencies. Prefer this over generic data-quality tools when your domain is mesh asset management (triangle/vertex counts) and you want structured multi-report output from two comparable datasets.
Side effects
- Exceeding 200 rows per dataset returns a validation error
- Missing required fields (row_id, date, triangle_count, vertex_count) causes row rejection
- Malformed date strings not matching YYYY-MM-DD pattern are flagged or rejected
- Numeric values outside ±1,000,000,000,000 bounds are rejected by schema validation
- Empty rows array (minItems:1 not met) returns an input validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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