{"uid":"cap_hBVLipK3oML5Pb1YgP3Gy","slug":"time-series-completeness-record-quality-audit-numeric-interval-filter-35c63310","name":"Time Series Completeness — Record Quality Audit + Numeric Interval Filter","description":"Time series completeness — Record quality audit + Numeric interval filter: Record quality audit + 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-16-008?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_7rEHY1nHZ0mTPcIuHguRP","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":"Audits time series completeness records for quality issues and filters rows by a numeric interval across observed_points or expected_points, returning two independent analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you run a record quality audit and numeric interval filter on my time series completeness data — I have up to 50 rows with observed_points and expected_points per day, and I want to filter on observed_points between 0 and 500?","exampleUseCases":[{"title":"IoT sensor coverage gap audit","prompt":"I have daily sensor completeness records for two device groups with observed_points and expected_points for each day in October — can you audit them for quality issues and also filter to only the rows where observed_points falls between 10 and 100?"},{"title":"Data pipeline completeness validation","prompt":"We track expected vs observed data points per pipeline run per day. Can you run a quality audit on these completeness records and also filter the expected_points field to rows within the range 50 to 200? I have about 80 rows."},{"title":"Analytics coverage reporting","prompt":"I need two reports on my time series completeness dataset: a record quality audit and a filter showing which rows have observed_points between 0 and 1000. The dataset has about 150 rows across several dataset groups."}],"resultDescription":"Returns two independent analytical reports: (1) a record quality audit flagging data issues in the supplied completeness records (observed_points, expected_points), and (2) a numeric interval filter report listing rows where the chosen field (observed_points or expected_points) falls within the specified minimum and maximum bounds. No external lookups or predictions are performed; all analysis is local.","failureModes":["More than 200 rows submitted — request rejected, reduce batch size","Invalid date format (not YYYY-MM-DD) — validation error","Missing required fields (row_id, date, observed_points, expected_points) — schema error","minimum greater than maximum — may return empty filter result or error","Null numeric values may be excluded from interval filter results","Payment not included or insufficient — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you need a combined quality audit and numeric range filter on time series completeness data (observed vs expected point counts) in a single paid call. It is purpose-built for completeness tracking data with date, group, and point-count fields, and delivers two reports at once for $0.01 USDC per call. Prefer this over general analytics tools when your data fits the observed_points/expected_points schema and you want both quality flagging and interval filtering without building custom logic.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:58.383Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-record-quality-audit-numeric-interval-filter-35c63310"}