{"uid":"cap_Z4Ixhj3jnJCyx1K0tFejU","slug":"time-series-completeness-numeric-row-ordering-numeric-interval-filter-f25e8dbb","name":"Time Series Completeness — Numeric Row Ordering + Numeric Interval Filter","description":"Time series completeness — Numeric row ordering + Numeric interval filter: Numeric row ordering + Numeric interval filter 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-16-113?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"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_dB1aT4pim1rpzXMCw573a","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":"Analyzes time series completeness records (observed vs expected points) using numeric row ordering and interval filtering, producing 2 analytical reports for up to 200 rows.","exampleAgentPrompt":"Check my time series completeness data for dataset groups 'sensors-a' and 'sensors-b' across October 2026 — order the rows numerically by observed_points and filter to rows where observed_points falls between 5 and 50. I have up to 20 rows to analyze.","exampleUseCases":[{"title":"Sensor data gap detection","prompt":"I have daily sensor readings for two device groups across the past month — can you order them by observed_points numerically and filter to only rows where observed_points is between 0 and 100, so I can see which days had incomplete data collection?"},{"title":"ETL pipeline completeness audit","prompt":"Run a completeness check on my ETL pipeline logs: I've got observed and expected record counts per day for 'pipeline-a' and 'pipeline-b', about 60 rows total. Order the results numerically and filter to rows where expected_points is between 500 and 2000."},{"title":"Multi-group metric coverage review","prompt":"Analyze these 80 rows of time series completeness records for my three reporting groups — order them by expected_points and filter down to the interval between 10 and 150, so I can identify which groups are consistently under-delivering on data points."}],"resultDescription":"Returns 2 analytical reports on the supplied time series completeness rows: one applying numeric row ordering (sorting rows by the selected field) and one applying a numeric interval filter (retaining only rows where the selected field falls within the specified minimum–maximum range). Both reports operate on the same input batch of up to 200 rows using local analytical engines, with no external data lookup or prediction.","failureModes":["More than 200 rows submitted — exceeds maxItems limit","Row missing required fields (row_id, date, observed_points, expected_points) — validation error","Invalid date format (not YYYY-MM-DD) — schema pattern rejection","minimum greater than maximum — logically empty filter interval producing no results","observed_points or expected_points outside allowed range (±1 trillion) — schema validation error"],"whenToPreferThis":"Choose this endpoint when you have pre-collected time series completeness records (observed vs expected data points) and need lightweight local analytical reports — specifically numeric ordering and interval filtering — without requiring any live data lookup, external enrichment, or predictive modeling. Best suited for batch offline QA checks on up to 200 rows across one or two dataset groups, where the $0.01 flat fee covers both reports in a single call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:58.986Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-numeric-row-ordering-numeric-interval-filter-f25e8dbb"}