{"uid":"cap_cmJ0QY9RG5DWiZ3ddYGG-","slug":"time-series-completeness-exact-row-deduplication-numeric-distribution-3c7a94ae","name":"Time Series Completeness — Exact Row Deduplication + Numeric Distribution","description":"Time series completeness — Exact row deduplication + Numeric distribution: Exact row deduplication + 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-16-039?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_R7ufffkG6bTIXzukb6_tm","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":"Runs two local analytical reports on time series completeness data: exact row deduplication and numeric distribution analysis for observed_points or expected_points, across up to 200 rows.","exampleAgentPrompt":"Can you deduplicate these time series completeness records and give me a numeric distribution report for observed_points? I have rows with row_id, dataset_group, date, observed_points, and expected_points — here's the data: row-1, group-a, 2026-10-01, observed=10, expected=1; row-2, group-b, 2026-10-02, observed=20, expected=2; row-3, group-a, 2026-10-03, observed=15, expected=3.","exampleUseCases":[{"title":"Deduplicate daily completeness records","prompt":"I've got a list of up to 200 time series completeness rows with row_id, dataset_group, date, observed_points, and expected_points — can you run exact row deduplication on them and also show me the numeric distribution of observed_points?"},{"title":"Distribution analysis for expected data points","prompt":"Here are my completeness tracking records across several dataset groups and dates. Can you analyze the numeric distribution of expected_points and also flag any exact duplicate rows in the data?"},{"title":"Audit completeness logs for duplicates and spread","prompt":"I need to audit my time series completeness logs — I have about 50 rows with observed and expected point counts per date and group. Run deduplication and give me a distribution breakdown for observed_points so I can see if there are anomalies."}],"resultDescription":"Returns two reports: (1) an exact row deduplication report identifying which rows among the submitted records are identical, and (2) a numeric distribution report (e.g. min, max, mean, quartiles, histogram bins) for the selected field (observed_points or expected_points), all computed locally without any external lookups.","failureModes":["More than 200 rows submitted — request rejected","Missing required fields (row_id, date, observed_points, expected_points) — validation error","date field does not match YYYY-MM-DD pattern — schema validation failure","field selector not one of 'observed_points' or 'expected_points' — enum validation error","Empty rows array — minItems violation","Payment not included or insufficient — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 time series completeness records and need both exact duplicate detection and numeric distribution analysis in a single low-cost call ($0.01 USDC for both reports). Ideal for data quality audits on completeness metrics (observed vs expected points) where no external lookup, live data, or predictive modeling is needed — pure local computation on supplied data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:43.841Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-exact-row-deduplication-numeric-distribution-3c7a94ae"}