{"uid":"cap_lK97kXsyPhC5P4yVZ7K9T","slug":"join-coverage-analytics-numeric-interval-filter-date-by-category-pivot-3585dbd1","name":"Join Coverage Analytics — Numeric Interval Filter + Date by Category Pivot","description":"Join coverage — Numeric interval filter + Date by category pivot: Numeric interval filter + 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-09-133?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"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_b3NAiBL4LZW64PiHKwo9a","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":"Filters join coverage rows (matched_rows or source_rows) by a numeric interval and produces a date-by-category pivot table, returning two analytical reports for up to 200 supplied rows.","exampleAgentPrompt":"Analyze my join coverage data — I have 4 rows with groups 'group-a' and 'group-b' across dates in October 2026, and I want to filter the matched_rows field to values between 10 and 30, then pivot the results by date and category.","exampleUseCases":[{"title":"ETL pipeline join quality review","prompt":"I've got join coverage records for two dataset groups over the past week — can you filter the matched_rows field to only show values between 5 and 50, then give me a date-by-category pivot so I can see how each group's join quality changes day by day?"},{"title":"Data reconciliation interval audit","prompt":"I need to audit my source_rows data — filter it to values between 0 and 100 and generate a pivot table breaking down the counts by dataset group and date so I can spot reconciliation gaps across groups."},{"title":"Coverage drop investigation by group","prompt":"Something looks off in my join coverage — filter the matched_rows field to anything between 0 and 15 and pivot by date and dataset_group so I can see which groups had low coverage and on which days."}],"resultDescription":"Returns two analytical reports: (1) a numeric interval-filtered subset of the supplied join coverage rows based on the chosen field (matched_rows or source_rows) and the min/max bounds, and (2) a date-by-category pivot table showing aggregated values broken down by date and dataset_group, all computed locally on the up-to-200 supplied rows without any external lookup.","failureModes":["Fewer than 1 row supplied — returns validation error","More than 200 rows submitted — exceeds maxItems limit","Invalid date format (not YYYY-MM-DD) — schema validation failure","minimum greater than maximum — may return empty filtered set with no error","Null values in matched_rows or source_rows — nulls handled per schema but may reduce pivot accuracy","Missing required fields (row_id, dataset_group, date) — schema validation error"],"whenToPreferThis":"Choose this endpoint when you have pre-collected join coverage records (matched_rows, source_rows) and need two local analytical reports — a numeric interval filter and a date-by-category pivot — without calling any external data source. Ideal for lightweight, low-cost ($0.01 USDC) on-demand analysis of up to 200 rows of ETL or data pipeline join metrics grouped by dataset category over time.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T18:54:26.448Z","isFirstParty":false,"canonicalSlug":"join-coverage-analytics-numeric-interval-filter-date-by-category-pivot-3585dbd1"}