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Time Series Completeness — String Normalisation + Exact Row Deduplication
x402
Time Series Completeness — String Normalisation + Exact Row Deduplication is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Normalises strings and deduplicates exact rows in time series completeness records (observed_points vs expected_points) for up to 200 rows, producing 2 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — String normalisation + Exact row deduplication: String normalisation + Exact row deduplication for supplied time series completeness records (observed_points, expected_points). 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 2 independent analytical reports covering the input rows: one addressing string normalisation (standardising casing, whitespace, and formatting in string fields like dataset_group and row_id), and one performing exact row deduplication (identifying and removing rows where all fields are identical). No live lookups, predictions, or external actions are performed — all processing is local and analytical.
Example use cases
Deduplicating daily sensor completeness logs
“I've got daily completeness logs for two sensor groups — 'sensor-north' and 'sensor-south' — covering the past week, with observed and expected data points for each date. Can you normalise the strings and strip out any exact duplicate rows before I load this into our dashboard?”
Cleaning pipeline completeness records before reporting
“Before I generate our monthly data quality report, I need to clean up these completeness records for our ETL pipeline — there are about 120 rows with dataset groups like 'pipeline-A' and 'pipeline-B' from October, and I suspect there are duplicates and inconsistent string casing. Can you normalise and deduplicate them?”
Standardising multi-group time series for QA audit
“We have completeness data from three dataset groups collected over 60 days — observed points and expected points per date — and I need to normalise all the string fields and remove any exact row duplicates before passing this to our QA team. Can you run the string normalisation and deduplication analysis on these rows?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-020?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to clean and deduplicate time series completeness records (observed vs expected point counts) locally without any external API calls, live data lookups, or predictions. It is ideal for pre-processing small-to-medium batches (up to 200 rows) of completeness data before ingestion into dashboards, QA pipelines, or reporting systems. Prefer this over general-purpose deduplication tools when your data specifically follows the date/dataset_group/observed_points/expected_points schema and you need both string normalisation and exact deduplication in a single low-cost call.
Side effects
- More than 200 rows submitted — request exceeds the maxItems limit and will be rejected
- Missing required fields (row_id, date, observed_points, expected_points) cause row-level validation errors
- Malformed date strings not matching YYYY-MM-DD pattern will fail schema validation
- Null values in observed_points or expected_points may affect deduplication logic depending on null handling
- Empty rows array (minItems: 1 not met) will be rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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