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Time Series Completeness Analytics — Quality Audit, Deduplication & Grouped Summary
x402
Time Series Completeness Analytics — Quality Audit, Deduplication & Grouped Summary is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Runs three independent local analytical reports on time series completeness records: record quality audit, exact row deduplication, and grouped numeric summary for observed vs expected data points.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + Exact row deduplication + Grouped numeric summary: Record quality audit + Exact row deduplication + Grouped numeric summary for supplied time series completeness records (observed_points, expected_points). 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 reports: (1) a record quality audit flagging nulls, malformed dates, and data issues; (2) an exact row deduplication report identifying and removing duplicate records; and (3) a grouped numeric summary of the selected field (observed_points or expected_points) aggregated by dataset_group. All analysis is performed locally on the supplied data with no external lookups.
Example use cases
Data pipeline completeness validation
“I have a batch of time series completeness records from our data pipeline — each row has a date, dataset group, and how many data points were observed vs expected. Can you audit the record quality, flag any exact duplicate rows, and give me a grouped numeric summary of observed_points across dataset groups?”
Pre-processing deduplication check
“Before I load this dataset into our warehouse, I need to deduplicate these time series rows and run a quality audit. The records have row IDs, dates, dataset groups, and observed vs expected point counts — up to 150 rows. Can you run all three reports using the expected_points field for the numeric summary?”
Daily completeness reporting by group
“I'm tracking data completeness across several dataset groups daily. Can you take these time series records — with observed_points and expected_points per date — audit their quality, remove any duplicate rows, and give me a grouped numeric summary so I can see which groups are falling short?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-211?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three simultaneous analytical passes — quality audit, deduplication, and grouped summary — on time series completeness data (observed vs expected points) in a single $0.01 call. Ideal for pre-ingestion validation pipelines, data quality monitoring workflows, or any scenario where you want to audit up to 200 rows of date-grouped completeness records without external API calls or live lookups.
Side effects
- Exceeding 200 rows returns an error — batch must be split
- Missing required fields (row_id, date, observed_points, expected_points) cause validation failure
- Dates not matching YYYY-MM-DD pattern are rejected
- Numeric values outside ±1,000,000,000,000 range are invalid
- Empty rows array (minItems:1 not met) causes request failure
- Invalid field enum value (not 'observed_points' or 'expected_points') rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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