{"uid":"cap_pGd-auIRAKKyjL-89ssbH","slug":"time-series-completeness-numeric-distribution-row-ordering-analysis-bc72a4c3","name":"Time Series Completeness — Numeric Distribution & Row Ordering Analysis","description":"Time series completeness — Numeric distribution + Numeric row ordering: Numeric distribution + 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-16-073?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_US5Rv-I2myzhSQf8J5R9G","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 data points) by generating both a numeric distribution report and a numeric row ordering report for up to 200 rows.","exampleAgentPrompt":"I have 4 rows of time series completeness data — for group-a on 2026-10-01 I observed 10 out of 1 expected points, on 2026-10-03 I observed 15 out of 3 expected; for group-b on 2026-10-02 I observed 20 out of 2 expected, on 2026-10-04 I observed 30 out of 4 expected — can you run both the numeric distribution report and row ordering analysis on the observed_points field?","exampleUseCases":[{"title":"Daily data pipeline completeness audit","prompt":"Here are 50 rows of daily completeness records for my ETL pipeline — each has a date, dataset group, observed_points, and expected_points. Can you run numeric distribution stats and a row ordering report on the observed_points field so I can see where coverage is thin?"},{"title":"Comparing sensor coverage across device groups","prompt":"I've got completeness records for three sensor groups across the last 30 days — observed readings versus expected readings per day. Run both the distribution and row ordering reports on expected_points for me so I can spot which groups are underperforming."},{"title":"Audit expected vs observed event counts for analytics","prompt":"I'm tracking event collection completeness for my analytics system — 120 rows with date, group name, observed_points, and expected_points. Give me the numeric distribution and ordering analysis on observed_points so I can see the range and ranked breakdown of how well we're capturing events."}],"resultDescription":"Returns two analytical reports for the supplied time series completeness rows: (1) a numeric distribution report summarizing statistical spread (e.g. min, max, mean, percentiles, histogram buckets) of the selected field (observed_points or expected_points), and (2) a numeric row ordering report that ranks rows by the selected field value. Both reports are computed locally from the supplied data with no external lookup.","failureModes":["Fewer than 1 row or more than 200 rows submitted — validation error","Missing required fields (row_id, dataset_group, date, observed_points, expected_points) on any row — schema error","Invalid date format (not YYYY-MM-DD) — pattern validation failure","Field value out of allowed numeric range (±1,000,000,000,000) — constraint error","Invalid field enum value (not 'observed_points' or 'expected_points') — enum validation error","Payment not included or insufficient — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you need both a distribution summary and a row-level ordering of numeric completeness metrics (observed vs expected points) for a time series dataset in a single $0.01 call. It is ideal for data quality audits, pipeline monitoring, and coverage analysis where you already have the completeness records in hand and need fast, local statistical reports without any external data fetching or prediction.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:48.506Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-numeric-distribution-row-ordering-analysis-bc72a4c3"}