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Feature Availability: Exact Row Deduplication + Keyed Dataset Comparison
x402
Feature Availability: Exact Row Deduplication + 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 2026-10-20).
Performs exact row deduplication and keyed dataset comparison on two sets of feature availability records (populated_features vs expected_features), returning two independent analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Exact row deduplication + Keyed dataset comparison: Exact row deduplication + Keyed dataset comparison for supplied feature availability records (populated_features, expected_features). 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 independent analytical reports: (1) an exact row deduplication report identifying duplicate rows within each supplied dataset, and (2) a keyed dataset comparison report showing row-by-row differences between the 'rows' and 'other' input datasets based on populated_features and expected_features values. No external data sources or live lookups are used — all analysis is performed on the supplied data locally.
Example use cases
ML pipeline feature completeness audit
“I have two snapshots of feature availability for my ML pipeline — the 'rows' dataset is what actually got populated, and the 'other' dataset is what was expected. Can you run exact row deduplication on both and give me a keyed comparison showing which dataset_group and date combinations have mismatches between populated_features and expected_features?”
Detecting duplicate records in feature store
“My feature store export has 150 rows across groups A and B from 2026-09-01 through 2026-10-15. Can you deduplicate them by row_id and dataset_group and also compare them against my expected feature counts to see where things don't line up?”
Dataset drift detection between two ETL runs
“I ran my ETL twice and got two slightly different feature availability outputs. Can you do an exact row dedup on both outputs and a keyed comparison using row_id, dataset_group, and date to flag rows where populated_features changed between the two runs?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-053?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both exact row deduplication and keyed dataset comparison on feature availability data in a single $0.01 call. It is ideal for ML/data engineering teams auditing feature store completeness, detecting ETL drift between two pipeline runs, or validating that populated feature counts match expected counts across dataset groups and dates — all without any external lookups or live data access. Prefer this over general-purpose data tools when your records conform to the row_id/dataset_group/date/populated_features/expected_features schema and you need two reports from one API call.
Side effects
- Input arrays exceed 200 rows — request rejected with validation error
- Missing required fields (row_id, dataset_group, date, populated_features, expected_features) cause schema validation failure
- Date field not matching YYYY-MM-DD pattern returns format error
- row_id exceeding 80 characters triggers length validation error
- Numeric values for populated_features or expected_features outside ±1,000,000,000,000 range rejected
- Empty rows array (minItems:1 not met) returns validation error
- Payment of 0.01 USDC not included or rejected causes 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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