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Brand Mention Inventories — Record Quality Audit + Explicit Missing-Value Fill
x402
Brand Mention Inventories — Record Quality Audit + Explicit Missing-Value Fill 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 and fills missing values (mention_count or source_count) in brand mention inventory datasets using local analytical engines, producing two independent reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Brand mention inventories — Record quality audit + Explicit missing-value fill: Record quality audit + Explicit missing-value fill for supplied brand mention inventories records (mention_count, source_count). 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
Returns two independent reports: (1) a record quality audit identifying issues such as nulls, invalid formats, or anomalies across the supplied brand mention rows; and (2) an explicit missing-value fill report showing which cells were imputed and what values were assigned using the chosen method (zero, mean, or median) for the specified field (mention_count or source_count).
Example use cases
Clean brand monitoring export before dashboard load
“I exported our brand mention data from the monitoring tool and there are gaps in the mention_count column — can you run a quality audit and fill those missing values using the median across the dataset? I have about 80 rows with company_group and date fields.”
Impute missing source counts for competitor analysis
“I'm comparing source_count across company groups but some rows have nulls — can you do a record quality audit and fill in the missing source_count values using the mean method for these 120 brand mention inventory rows?”
Validate brand mention data before quarterly report
“Before I hand this brand mention inventory off for the quarterly report, can you run a quality audit on all 150 rows and fill any missing mention_count values with zero so nothing looks undefined?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-18-019?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have brand mention inventory data (with mention_count and source_count fields) that needs both a quality audit and missing-value imputation in a single call. It is ideal for preprocessing brand monitoring exports before analytics, dashboards, or reporting pipelines. Prefer it over generic data cleaning tools when your data fits the brand mention schema and you need two structured reports simultaneously for up to 200 rows at very low cost.
Side effects
- Exceeding 200 rows returns an error — batch must be split
- Invalid date format (not YYYY-MM-DD) causes row rejection
- Numeric values outside allowed range trigger validation errors
- Empty rows array rejected (minimum 1 row required)
- Unsupported method or field enum value causes request failure
- Missing required fields (row_id, company_group, date) cause schema validation errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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