{"uid":"cap_LF9kAzkg5iCfI8KOCcjcP","slug":"join-coverage-analytics-numeric-distribution-date-by-category-pivot-f28e1ce5","name":"Join Coverage Analytics: Numeric Distribution + Date-by-Category Pivot","description":"Join coverage — Numeric distribution + Date by category pivot: Numeric distribution + 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-09-083?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_4v3lbcfyCmxcS5XAttbcy","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 local analytical reports — a numeric distribution and a date-by-category pivot — for join coverage records containing matched_rows and source_rows fields, for up to 200 rows.","exampleAgentPrompt":"Analyze my join coverage data — I have 150 rows with row_id, dataset_group, date, matched_rows, and source_rows fields — and give me a numeric distribution plus a date-by-category pivot on the matched_rows field.","exampleUseCases":[{"title":"ETL pipeline join quality audit","prompt":"I have 80 records from my ETL pipeline join step with dataset groups like 'customers' and 'orders', each with a date, matched_rows, and source_rows. Can you run a numeric distribution and date-by-category pivot on matched_rows so I can see how well rows are matching across groups over time?"},{"title":"Data warehouse coverage comparison","prompt":"I want to compare join coverage across my data warehouse partitions — I have up to 200 rows with dataset_group, date, source_rows, and matched_rows. Please give me both a numeric distribution and a date-by-category pivot on source_rows."},{"title":"Daily join match rate trend analysis","prompt":"Can you analyze my join coverage records from the last quarter — about 120 rows tagged by dataset_group and date, with matched_rows and source_rows counts — and produce a numeric distribution plus a category-by-date pivot so I can spot which groups have low match rates on which days?"}],"resultDescription":"Two analytical reports computed locally from the supplied data: (1) a numeric distribution of the selected field (matched_rows or source_rows) showing statistical spread across records, and (2) a date-by-category pivot aggregating the selected field by date and dataset_group. No external lookups are performed; results are derived entirely from the submitted rows.","failureModes":["Fewer than 1 row submitted — request rejected","More than 200 rows submitted — request rejected","Invalid date format (not YYYY-MM-DD) — validation error","Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) — schema validation error","Numeric values outside allowed range (±1,000,000,000,000) — rejected","Invalid field enum value (not matched_rows or source_rows) — rejected"],"whenToPreferThis":"Choose this endpoint when you have join coverage data (matched_rows and source_rows per record) and need two quick analytical reports — a numeric distribution and a date-by-category pivot — computed locally without any external data lookups. Ideal for small-to-medium datasets (up to 200 rows) where you want cheap ($0.01 USDC for both reports), self-contained analytics on dataset group join quality over time.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T18:54:43.456Z","isFirstParty":false,"canonicalSlug":"join-coverage-analytics-numeric-distribution-date-by-category-pivot-f28e1ce5"}