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Brand Mention Inventories — Record Quality Audit, Exact Row Deduplication & Numeric Distribution
x402
Brand Mention Inventories — Record Quality Audit, Exact Row Deduplication & Numeric Distribution 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).
Audits record quality, deduplicates exact rows, and computes numeric distributions for brand mention inventory data (mention_count, source_count) across up to 200 rows in three independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Brand mention inventories — Record quality audit + Exact row deduplication + Numeric distribution: Record quality audit + Exact row deduplication + Numeric distribution 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: (1) a record quality audit flagging completeness and validity issues in the submitted rows, (2) an exact row deduplication report identifying fully identical records, and (3) a numeric distribution summary for the selected field (mention_count or source_count), all computed locally against the supplied data without any external lookups.
Example use cases
Pre-analysis brand data quality check
“I have 120 rows of brand mention data for three company groups collected over the past month, each with a mention_count and source_count. Can you audit the record quality, find any exact duplicate rows, and show me the numeric distribution for mention_count before I hand this off for reporting?”
Deduplicating scraped brand mention logs
“We scraped brand mentions from multiple feeds and I think there are duplicate records in our dataset. Can you run an exact row deduplication plus a record quality audit on these 80 rows — the key columns are mention_count and source_count per company group per date?”
Distribution analysis for source coverage reporting
“For our weekly brand monitoring report, can you compute the numeric distribution of source_count across our 200 brand mention inventory rows and also flag any data quality issues or exact duplicates at the same time?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-18-210?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to simultaneously validate data quality, remove exact duplicate records, and understand the numeric spread of brand mention or source count data in a single low-cost call. It is ideal as a pre-processing step before downstream brand analytics, especially when working with aggregated brand mention inventories grouped by company and date. Not suitable for live brand monitoring, predictive analytics, or datasets exceeding 200 rows.
Side effects
- Input array exceeds 200 rows — request will be rejected or truncated
- Missing required fields (row_id, company_group, date, mention_count, source_count) causes validation errors
- Invalid date format (not YYYY-MM-DD) triggers schema validation failure
- Numeric values outside ±1,000,000,000,000 range rejected by schema
- Empty rows array rejected (minItems: 1)
- Invalid field enum value (must be 'mention_count' or 'source_count') causes error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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