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Brand Mention Inventories — Numeric Interval Filter + IQR Outlier Flags
x402
Brand Mention Inventories — Numeric Interval Filter + IQR Outlier Flags 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).
Applies a numeric interval filter and IQR-based outlier detection to user-supplied brand mention inventory records (mention_count, source_count), returning two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Brand mention inventories — Numeric interval filter + IQR outlier flags: Numeric interval filter + IQR outlier flags 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
Two analytical reports: one showing rows that pass the specified numeric interval filter on the chosen field (mention_count or source_count), and one flagging rows identified as IQR outliers. Results are computed locally from the supplied data with no external lookups.
Example use cases
Outlier detection in PR campaign data
“I've got 80 rows of brand mention data from our Q4 PR campaign — each row has a row_id, company_group, date, mention_count, and source_count. Can you filter mention_count between 10 and 500 and flag any IQR outliers across all company groups?”
Source count range filtering for competitor analysis
“Here are 150 brand mention inventory records for three competitor groups. Please filter on the source_count field, keeping only rows between 1 and 200, and flag any statistical outliers using IQR — I want two reports back.”
Data quality check on mention inventory before reporting
“Before I send this brand mention dataset to my manager, can you run an interval filter on mention_count from 0 to 1000 and IQR outlier flags on it? I have 60 rows with row_id, date, company_group, mention_count, and source_count.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-18-130?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 brand mention inventory records and need both a numeric range filter and IQR-based outlier detection in a single call, without any external data lookup or prediction. Ideal for pre-reporting data quality checks, anomaly detection in PR or marketing datasets, and batch statistical analysis of mention or source count time series.
Side effects
- Missing required row fields (row_id, company_group, date, mention_count, source_count) causes validation errors
- Exceeding 200 rows returns an error or truncation
- Invalid date format (non-YYYY-MM-DD) fails schema validation
- Minimum value greater than maximum value may produce empty filter results
- Null numeric values in the target field may be excluded or cause partial report gaps
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| maximum | number | |
| minimum | number |
Tags
x402
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