{"uid":"cap_NKgeR6BEDUbR-kzc65kE4","slug":"join-coverage-analytics-daily-sum-series-base-100-series-543bb93c","name":"Join Coverage Analytics — Daily Sum Series + Base-100 Series","description":"Join coverage — Daily sum series + Base-100 series: Daily sum series + Base-100 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-09-138?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"},"base_index":{"type":"integer","default":0,"maximum":199,"minimum":0}}},"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_ptZgYEiZ1PWi9mUdkLn0F","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":"Generates two analytical reports (daily sum series and base-100 indexed series) for join coverage data tracking matched_rows and source_rows across dates and dataset groups.","exampleAgentPrompt":"I have daily join coverage data for two dataset groups — group-a and group-b — from October 1 to October 4, with matched_rows and source_rows recorded per day. Can you run the daily sum series and base-100 index reports on the matched_rows field, using row 0 as the base index?","exampleUseCases":[{"title":"ETL pipeline join quality monitoring","prompt":"I want to see how my ETL job's join coverage has trended over the past week — here are the daily matched_rows and source_rows for my 'customers' and 'orders' dataset groups. Can you generate the daily sum series and a base-100 indexed series on matched_rows, starting from the first day?"},{"title":"Comparing source vs matched row counts over time","prompt":"I have 30 days of join records for three dataset groups and I want to analyze source_rows rather than matched_rows — can you compute both the daily sum series and the base-100 index series for source_rows, starting from index 0?"},{"title":"Normalizing join coverage for stakeholder reporting","prompt":"My team needs a normalized view of join coverage for a board report — I have 60 daily rows across two groups with matched_rows and source_rows. Please run the daily sum and base-100 reports on matched_rows using day 5 as the base index."}],"resultDescription":"Two analytical reports: (1) a daily sum series aggregating the selected field (matched_rows or source_rows) per date across dataset groups, and (2) a base-100 indexed series normalizing the selected field relative to a specified base index row, enabling trend comparison. Up to 200 input rows are supported per call.","failureModes":["Input array exceeds 200 rows — returns validation error","Missing required fields (row_id, date, dataset_group, matched_rows, source_rows) on any row — returns schema error","Invalid date format (not YYYY-MM-DD) — returns pattern validation error","base_index value out of range (>=number of rows) — returns index out-of-bounds error","Numeric values outside allowed range (±1 trillion) — returns range validation error","Payment not provided or insufficient — returns 402 Payment Required"],"whenToPreferThis":"Choose this endpoint when you have tabular join coverage data (matched_rows vs source_rows) keyed by date and dataset group and need both a daily aggregated sum series and a base-100 normalized trend series in a single call. It is ideal for data engineers monitoring ETL join quality, analysts building time-series reports on data pipeline completeness, or anyone who wants two complementary analytical views (absolute and indexed) on join coverage without standing up their own analytics engine.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:33.242Z","isFirstParty":false,"canonicalSlug":"join-coverage-analytics-daily-sum-series-base-100-series-543bb93c"}