{"uid":"cap__KH0ZwsS35m7TSAHUiZgX","slug":"join-coverage-record-quality-audit-daily-sum-series-b64970f8","name":"Join Coverage — Record Quality Audit + Daily Sum Series","description":"Join coverage — Record quality audit + Daily sum series: Record quality audit + Daily sum series for supplied join coverage records (matched_rows, source_rows). 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-09-009?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","matched_rows","source_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"matched_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_rows":1,"matched_rows":10,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","source_rows":2,"matched_rows":20,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","source_rows":3,"matched_rows":15,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","source_rows":4,"matched_rows":30,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["matched_rows","source_rows"],"type":"string","default":"matched_rows"}}},"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_k3R6hCNHNpLvsx_sYtenu","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 join coverage data: a record quality audit and a daily sum series for matched_rows or source_rows, over up to 200 supplied rows.","exampleAgentPrompt":"Run a record quality audit and daily sum series on my join coverage data — I have rows with dataset groups 'sales' and 'returns', dates from 2024-01-01 to 2024-01-07, matched_rows and source_rows filled in, and I want to analyze the matched_rows field. Up to 50 rows total.","exampleUseCases":[{"title":"Daily join completeness trend check","prompt":"I have 120 join coverage records for my orders-to-inventory dataset, each with a date, matched_rows, and source_rows — can you run a daily sum series on matched_rows so I can see how total matches trend day by day?"},{"title":"Data quality gate before pipeline promotion","prompt":"Before I promote this ETL job to production, audit the record quality of my join coverage output — I'll supply up to 80 rows with row_id, dataset_group, date, matched_rows and source_rows, and I need the quality report to catch nulls, out-of-range values, or anomalies."},{"title":"Compare source vs matched row sums by date","prompt":"I want to understand coverage gaps in my customer-to-transaction join — can you run the record quality audit AND a daily sum series on source_rows for my 60 rows across two dataset groups so I can see where totals drop off?"}],"resultDescription":"Returns two independent analytical reports: (1) a record quality audit flagging issues such as nulls, out-of-range values, or anomalies in the supplied join coverage rows; and (2) a daily sum series aggregating the selected field (matched_rows or source_rows) by date. Both reports are computed locally from the supplied data with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) cause row-level rejection","Invalid date format (not YYYY-MM-DD) triggers schema validation failure","Numeric values outside ±1,000,000,000,000 are rejected","Empty rows array returns an error","Payment of 0.01 USDC required per call; missing payment results in 402 response"],"whenToPreferThis":"Choose this endpoint when you need both a data quality audit and a daily aggregation summary for join coverage records in a single $0.01 call, without requiring any external data lookups or live connections. It is best suited for batch validation of ETL join outputs, pipeline QA gates, or exploratory analysis of matched vs source row counts over time for up to 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:40.767Z","isFirstParty":false,"canonicalSlug":"join-coverage-record-quality-audit-daily-sum-series-b64970f8"}