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Brand Mention Inventories — Record Quality Audit + Exact Row Deduplication + Adjacent Row Changes
x402
Brand Mention Inventories — Record Quality Audit + Exact Row Deduplication + Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-16).
Runs three local analytical reports on brand mention inventory data: a record quality audit, exact row deduplication, and adjacent row change detection — for up to 200 rows of mention_count or source_count data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Brand mention inventories — Record quality audit + Exact row deduplication + Adjacent row changes: Record quality audit + Exact row deduplication + Adjacent row changes for supplied brand mention inventories records (mention_count, source_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
Returns three independent analytical reports derived locally from the submitted data: (1) a record quality audit flagging malformed or missing values, (2) an exact row deduplication report identifying identical records, and (3) an adjacent row changes report showing how mention_count or source_count shifts between consecutive rows. No external lookups or predictions are made.
Example use cases
Deduplication check before reporting
“I'm about to compile our monthly brand mention report but I think there are duplicate entries in the dataset. Can you run the exact row deduplication and record quality audit on these brand mention records — the data has row_id, company_group, date, and mention_count columns — and tell me which rows are duplicates?”
Adjacent change detection for anomaly spotting
“I want to see where our brand mention counts jump significantly between consecutive dates. Can you run the adjacent row changes report on this brand mention inventory dataset with mention_count as the field to analyze?”
Data quality audit before pipeline ingestion
“Before I feed this brand mention inventory data into our analytics pipeline, can you run all three quality checks — record quality audit, deduplication, and adjacent row changes — on this set of up to 150 rows with company_group, date, and source_count fields?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-18-217?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three simultaneous data quality checks — quality audit, deduplication, and adjacent row change detection — on brand mention inventory data (mention_count or source_count) for up to 200 rows at minimal cost ($0.01 USDC). Prefer it over general-purpose analytics when your data conforms to the brand mention schema (row_id, company_group, date, mention_count, source_count) and you want all three reports in a single call without external data dependencies.
Side effects
- Submitting more than 200 rows returns an error or truncated results
- Missing required fields (row_id, company_group, date, mention_count, source_count) causes schema validation failure
- Invalid date format (not YYYY-MM-DD) triggers pattern validation error
- Selecting an invalid field enum value other than 'mention_count' or 'source_count' causes rejection
- Payment of 0.01 USDC via x402 must succeed before processing begins
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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