{"uid":"cap_pa5YpzdBdKiOLRBqh7g1O","slug":"time-series-completeness-duplicate-key-report-numeric-distribution-1be1a67c","name":"Time Series Completeness — Duplicate Key Report + Numeric Distribution","description":"Time series completeness — Duplicate key report + Numeric distribution: Duplicate key report + Numeric distribution 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-16-055?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_6afd1LvQ_j6dgHHJ_2-0W","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_points, expected_points) to produce a duplicate key report and numeric distribution for up to 200 rows.","exampleAgentPrompt":"Check my time series completeness records for duplicate row IDs and give me a numeric distribution of the observed_points field — here are the rows: [{\"row_id\":\"r1\",\"dataset_group\":\"sensors\",\"date\":\"2024-01-01\",\"observed_points\":95,\"expected_points\":100},{\"row_id\":\"r1\",\"dataset_group\":\"sensors\",\"date\":\"2024-01-02\",\"observed_points\":80,\"expected_points\":100}].","exampleUseCases":[{"title":"Auditing sensor data completeness logs","prompt":"I have 150 daily completeness records for my IoT sensor datasets — can you run a duplicate key check and show me the numeric distribution of observed_points across all of them?"},{"title":"Validating ETL pipeline completeness records","prompt":"After my ETL run, I have completeness rows with row_id, dataset_group, date, observed_points, and expected_points. Check them for any duplicate keys and give me a distribution breakdown of the expected_points field."},{"title":"Monitoring reporting data gaps","prompt":"Here are my completeness records for last quarter's reporting datasets. Can you flag any duplicate row IDs and tell me the spread of observed_points values — min, max, distribution buckets, that sort of thing?"}],"resultDescription":"Returns two independent analytical reports: (1) a duplicate key report identifying any rows sharing the same key within the supplied records, and (2) a numeric distribution report for the selected field (observed_points or expected_points), covering spread, frequency, and statistical distribution — all computed locally from the supplied data, up to 200 rows.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, date, observed_points, expected_points) — validation error","Invalid date format (not YYYY-MM-DD pattern) — schema validation failure","Numeric values outside allowed range (±1,000,000,000,000) — rejected","Empty rows array — minItems:1 constraint violation"],"whenToPreferThis":"Choose this endpoint when you need to simultaneously detect duplicate keys and understand the numeric distribution of completeness metrics (observed vs expected points) in a time series dataset, without requiring any external lookup or live data — ideal for lightweight, self-contained data quality audits on batches up to 200 rows at very low cost ($0.01 USDC for both reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:40.263Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-duplicate-key-report-numeric-distribution-1be1a67c"}