{"uid":"cap_cvQXQh7nJvAUoEisqsdFI","slug":"join-coverage-numeric-row-ordering-iqr-outlier-flags-42127986","name":"Join Coverage — Numeric Row Ordering + IQR Outlier Flags","description":"Join coverage — Numeric row ordering + IQR outlier flags: Numeric row ordering + IQR outlier flags 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-09-119?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_Cu-Uk9THrQmxkQ69bkoBN","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 records (matched_rows, source_rows) by applying numeric row ordering and IQR-based outlier detection, producing 2 analytical reports for up to 200 rows.","exampleAgentPrompt":"Analyze my join coverage data for outliers — here are 4 rows across groups 'group-a' and 'group-b' with dates from 2026-10-01 to 2026-10-04, matched_rows values of 10, 20, 15, and 30, and source_rows of 1, 2, 3, and 4; please run the numeric row ordering and IQR outlier flagging on the matched_rows field.","exampleUseCases":[{"title":"Audit ETL pipeline join ratios","prompt":"I've got 50 rows of join coverage output from my ETL pipeline with matched_rows and source_rows per day — can you rank them numerically and flag any IQR outliers in the matched_rows field so I know which days had anomalous join behavior?"},{"title":"Compare join coverage across dataset groups","prompt":"Here are 120 join coverage records split across three dataset groups over the last quarter. Run numeric ordering and IQR outlier detection on the source_rows field so I can see which groups consistently fall outside normal range."},{"title":"QA check before data warehouse load","prompt":"Before we load this join coverage batch into the warehouse, can you order these 80 rows by their matched_rows values and flag any IQR outliers? The data has row IDs, dates, and group labels already included."}],"resultDescription":"Two analytical reports covering: (1) numeric row ordering of the selected field (matched_rows or source_rows) across the supplied rows, and (2) IQR-based outlier flags identifying which rows fall outside the interquartile range. Reports are computed locally from the supplied data with no external lookups.","failureModes":["Fewer than 1 row supplied — request rejected","More than 200 rows submitted — exceeds limit, request rejected","Invalid date format (not YYYY-MM-DD) — schema validation error","Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) — validation failure","Null values in numeric fields may affect IQR computation","Invalid field enum value (not 'matched_rows' or 'source_rows') — schema error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 join coverage records and need both numeric ordering and IQR-based outlier detection in a single call with no external dependencies. Ideal for data engineers auditing ETL pipelines, QA workflows checking join ratios before loads, or analysts flagging anomalous coverage across dataset groups. The fixed $0.01 cost covers 2 reports per call, making it cost-effective for batch audits of join data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:39.602Z","isFirstParty":false,"canonicalSlug":"join-coverage-numeric-row-ordering-iqr-outlier-flags-42127986"}