{"uid":"cap_sD4hZBDB-g3UQaCsOKuqx","slug":"join-coverage-analytics-numeric-row-ordering-secondary-measure-23b7de5f","name":"Join Coverage Analytics: Numeric Row Ordering + Secondary-Measure Weighted Mean","description":"Join coverage — Numeric row ordering + Secondary-measure weighted mean: Numeric row ordering + Secondary-measure weighted mean 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-09-121?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_4JVAujv4NywRKKcmCroGl","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":"Computes numeric row ordering and secondary-measure weighted mean statistics for join coverage data (matched_rows, source_rows) across up to 200 supplied rows, returning two independent analytical reports.","exampleAgentPrompt":"I have join coverage data for four dataset groups — can you rank the rows numerically by matched_rows and compute the weighted mean using matched_rows as the primary field? Here are the records: row-1 (group-a, 2026-10-01, matched=10, source=1), row-2 (group-b, 2026-10-02, matched=20, source=2), row-3 (group-a, 2026-10-03, matched=15, source=3), row-4 (group-b, 2026-10-04, matched=30, source=4).","exampleUseCases":[{"title":"ETL pipeline join quality audit","prompt":"I ran a join between two tables last week and have 50 rows of coverage results — can you rank them numerically by matched_rows and calculate the weighted mean so I can see which dataset groups are performing best?"},{"title":"Data source coverage comparison","prompt":"I want to compare join coverage across three dataset groups over the past month — please order the 80 records by source_rows and compute the weighted mean for source_rows so I can spot which group has the weakest coverage."},{"title":"Daily join health summary","prompt":"Here are today's join coverage records for group-alpha and group-beta across 10 dates — rank them by matched_rows and give me the weighted mean, so I have two summary reports to share with the data team."}],"resultDescription":"Two independent analytical reports: (1) a numeric row ordering of the supplied join coverage records ranked by the selected field (matched_rows or source_rows), and (2) a secondary-measure weighted mean computed across those records, grouped by dataset_group and date context. No external lookups or predictions are made — all computation is performed on the supplied data.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) — validation error","Date field not in YYYY-MM-DD format — pattern validation failure","Numeric values outside allowed range (±1,000,000,000,000) — schema rejection","Empty rows array — minItems:1 violation","Invalid field enum value (not 'matched_rows' or 'source_rows') — rejected"],"whenToPreferThis":"Choose this endpoint when you have pre-collected join coverage data (matched_rows + source_rows per row) and need two specific statistical outputs — numeric row ordering and a weighted mean — without any external data lookup. It is ideal for data engineers auditing ETL pipeline join quality, analysts summarizing coverage metrics across dataset groups, or any workflow where you want a fast, deterministic, offline computation on up to 200 join coverage records at low cost ($0.01 USDC for both reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:39.885Z","isFirstParty":false,"canonicalSlug":"join-coverage-analytics-numeric-row-ordering-secondary-measure-23b7de5f"}