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Brand Mention Inventories — Record Quality Audit + String Normalisation + Numeric Distribution
x402
Brand Mention Inventories — Record Quality Audit + String Normalisation + 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 2026-10-20).
Runs three local analytical reports (record quality audit, string normalisation, and numeric distribution) on brand mention inventory data rows containing mention_count and source_count fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Brand mention inventories — Record quality audit + String normalisation + Numeric distribution: Record quality audit + String normalisation + 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
Three independent analytical reports: (1) a record quality audit flagging missing values, malformed dates, or structural issues; (2) a string normalisation report standardising company_group and other string fields; and (3) a numeric distribution summary (min, max, mean, percentiles, etc.) for the selected numeric field (mention_count or source_count). All computed locally on the supplied rows with no external lookups.
Example use cases
Brand data quality pre-flight check
“Before I load this brand mention dataset into our dashboard, can you audit the record quality, normalise the company_group strings, and show me the numeric distribution of mention_count? I have about 150 rows with row_id, company_group, date, mention_count, and source_count.”
Investigating source count distribution anomalies
“I want to understand the spread of source_count values across my brand mention inventory rows — can you run the record quality audit, string normalisation, and numeric distribution report using source_count as the target field? Here are my 80 rows.”
Cleaning multi-group brand mention export
“I exported brand mention data for two company groups over the past quarter — about 120 rows total — and I need you to flag any quality issues, normalise the group name strings, and give me a numeric distribution of mention_count so I can spot any outliers before I present it.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-18-193?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 brand mention inventory rows and need three bundled analytical reports — record quality audit, string normalisation, and numeric distribution — in a single low-cost call. It is best suited for pre-processing or QA pipelines where no external data enrichment, prediction, or live lookup is needed. Prefer it over separate single-report endpoints when all three report types are required simultaneously for the same dataset.
Side effects
- Input array empty or exceeds 200 rows — request rejected
- Required fields (row_id, company_group, date, mention_count, source_count) missing from row objects — validation error
- date field not matching YYYY-MM-DD pattern — schema validation failure
- numeric values outside allowed range (±1,000,000,000,000) — rejection
- Invalid enum value for field parameter (must be mention_count or source_count) — error response
- Payment of 0.01 USDC not received — 402 Payment Required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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