{"uid":"cap_Tch2Q1pYov4FVwFKkp6Zx","slug":"join-coverage-analytics-daily-sum-series-date-by-category-pivot-1aa68d12","name":"Join Coverage Analytics: Daily Sum Series + Date by Category Pivot","description":"Join coverage — Daily sum series + Date by category pivot: Daily sum series + Date by category pivot 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-09-143?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_Y6KdiKJX6-YcTFGbCY_57","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 join coverage data (matched_rows, source_rows) by generating a daily sum time series and a date-by-category pivot table, returning two independent analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you run two analytics reports on my join coverage data — a daily sum series and a date-by-category pivot — for these rows tracking matched_rows across group-a and group-b from 2026-10-01 to 2026-10-04? Use matched_rows as the field to analyze.","exampleUseCases":[{"title":"ETL pipeline join rate audit","prompt":"I have 4 days of join coverage records for two dataset groups and I want to see both a daily sum time series and a pivot of matched_rows by date and group — can you run those two reports for me? The rows span 2026-10-01 to 2026-10-04 with groups alpha and beta, and I want to focus on matched_rows."},{"title":"Source vs matched row comparison","prompt":"Run a daily sum series and a date-by-category pivot on my source_rows data — I have about 150 records spread across three dataset groups over the past month and I want to understand how source row counts vary by group and day."},{"title":"Data quality monitoring over time","prompt":"I want to monitor how my join coverage has been trending — can you take my 200 rows of join coverage data with matched_rows and source_rows across multiple groups and give me both a daily aggregated time series and a pivot table breaking it down by dataset_group and date?"}],"resultDescription":"Two independent analytical reports: (1) a daily sum time series showing aggregated values of the selected field (matched_rows or source_rows) across all records per day, and (2) a date-by-category pivot table cross-tabulating the selected field by date and dataset_group. Both reports are computed locally from the supplied rows with no external lookups.","failureModes":["Missing required row fields (row_id, date, dataset_group, matched_rows, source_rows) returns a validation error","Date strings not matching YYYY-MM-DD pattern are rejected","Submitting more than 200 rows exceeds the maxItems limit and is rejected","Submitting 0 rows (empty array) fails minItems constraint","Payment failure or insufficient USDC balance prevents processing","Numeric values outside ±1,000,000,000,000 are rejected"],"whenToPreferThis":"Choose this endpoint when you need two complementary analytical views — a time series and a pivot — on join coverage metrics in a single $0.01 call. Ideal for data engineers monitoring ETL pipeline health, auditing matched vs source row ratios over time, or exploring category-level trends without setting up a local analytics environment. Best when you already have structured join coverage records with dates and group labels and want quick, self-contained analysis without external data lookups.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:45.786Z","isFirstParty":false,"canonicalSlug":"join-coverage-analytics-daily-sum-series-date-by-category-pivot-1aa68d12"}