{"uid":"cap_toXGrJP0hyjfgHFhYm4FP","slug":"time-series-completeness-deduplication-and-numeric-ordering-6d816af6","name":"Time Series Completeness Deduplication and Numeric Ordering","description":"Time series completeness — Exact row deduplication + Numeric row ordering: Exact row deduplication + Numeric row ordering 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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-042?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","observed_points","expected_points"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"expected_points":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"observed_points":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","expected_points":1,"observed_points":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","expected_points":2,"observed_points":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_points":3,"observed_points":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_points":4,"observed_points":30}],"maxItems":200,"minItems":1},"field":{"enum":["observed_points","expected_points"],"type":"string","default":"observed_points"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_lGsm7pIiIyGDjoi-SL7rJ","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Deduplicates and numerically orders time series completeness records (observed_points, expected_points) for up to 200 rows across 2 independent reports.","exampleAgentPrompt":"I have a batch of time series completeness records with observed_points and expected_points for dataset groups — can you deduplicate exact duplicate rows and sort them by numeric order? I've got about 50 rows across groups like 'sensor-a' and 'sensor-b' with dates in 2025, and I want both the observed_points and expected_points field reports.","exampleUseCases":[{"title":"Sensor data completeness audit","prompt":"I'm auditing sensor telemetry completeness — I have 120 rows with row_id, date, dataset_group, observed_points and expected_points, and I suspect there are duplicates. Can you deduplicate exact duplicate rows and give me a numerically ordered completeness report for the observed_points field?"},{"title":"ETL pipeline completeness validation","prompt":"My ETL pipeline output has completeness tracking rows for two dataset groups called 'pipeline-raw' and 'pipeline-processed' — about 80 rows with dates from 2025-01-01 onward. Please deduplicate any exact duplicate rows and sort everything by numeric order so I can review the expected_points report."},{"title":"Multi-group completeness comparison cleanup","prompt":"I have completeness records for three dataset groups — 'batch-a', 'batch-b', and 'batch-c' — spanning 60 rows with observed and expected point counts. Can you strip out exact duplicate rows and return two numerically ordered reports, one for observed_points and one for expected_points?"}],"resultDescription":"The endpoint returns two independent analytical reports on the supplied time series completeness records: one covering exact row deduplication (identifying and removing exact duplicate rows) and one covering numeric ordering of the specified field (observed_points or expected_points). Both reports are based solely on the supplied data, with no external lookups or predictions.","failureModes":["More than 200 rows submitted — exceeds maxItems limit","Missing required fields (row_id, date, observed_points, expected_points) on any row","Date field not in YYYY-MM-DD format — fails pattern validation","Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000)","Empty rows array — minItems constraint violated","Payment not completed — x402 payment required before processing"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local deduplication and numeric ordering of time series completeness records (observed vs expected points) without any external data lookups or ML predictions. Ideal for data quality checks on small-to-medium batches (up to 200 rows) where exact duplicate removal and sorted ordering are the primary goals. Prefer alternatives if you need forecasting, anomaly detection, or completeness gap prediction.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:59.520Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-deduplication-and-numeric-ordering-6d816af6"}