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Material Libraries — String Normalisation + Keyed Dataset Comparison
x402
Material Libraries — 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 2026-10-31).
Normalises string fields and compares two keyed datasets of material library records (material_count, texture_mb) producing two analytical reports for up to 200 rows each.
Price / call$0.01
Success—
StatusUnknown
How this service works
Material libraries — String normalisation + Keyed dataset comparison: String normalisation + Keyed dataset comparison for supplied material libraries records (material_count, texture_mb). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Two analytical reports: one covering string normalisation results (cleaned and standardised string fields like asset_group), and one covering keyed dataset comparison (row-by-row differences in material_count and texture_mb between the two supplied datasets), covering up to 200 rows per dataset. No external lookups or predictions are made.
Example use cases
Audit material count changes between sprints
“I have two exports of our material library from last sprint and this sprint, each about 80 rows with asset_group, date, material_count, and texture_mb. Can you normalise the asset group names and compare the two datasets row-by-row to show me what changed?”
Detect texture size drift across asset groups
“Here are two snapshots of our texture asset records — one from last month and one from today — each with row_id, asset_group, date, material_count, and texture_mb. Normalise the strings and give me a comparison report showing which rows have different texture_mb values.”
Validate consistency of merged material libraries
“We merged two studios' material libraries and I need to check for discrepancies. I'll give you both datasets — up to 150 rows each — with row_id, asset_group, date, material_count, and texture_mb. Please normalise the string fields and run a keyed comparison to flag any mismatches.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-creative-03-036?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have two structured material library datasets you want to compare keyed by row ID, and also need string normalisation applied to categorical fields like asset_group. It is a self-contained offline analytical tool — ideal when you do not need live data lookup, ML predictions, or external enrichment, and when your dataset fits within 200 rows. Prefer it over generic comparison tools when your data schema matches material_count and texture_mb fields specifically.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, asset_group, date, material_count, texture_mb) causing validation errors
- Mismatched row_id keys between datasets resulting in unmatched rows in comparison report
- Invalid date format (not YYYY-MM-DD) causing parsing errors
- Null numeric values for both material_count and texture_mb reducing comparison utility
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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