{"uid":"cap_vrX4gxSjkEivvJm0_LKrP","slug":"join-coverage-analytics-string-normalisation-iqr-outlier-flags-deb6f9e4","name":"Join Coverage Analytics: String Normalisation + IQR Outlier Flags","description":"Join coverage — String normalisation + IQR outlier flags: String normalisation + 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-09-032?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_V6MzDngAdjgNs9u64rel9","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":"Normalises string fields and flags IQR-based outliers in join coverage records (matched_rows, source_rows) for up to 200 rows, producing 2 analytical reports.","exampleAgentPrompt":"Can you run string normalisation and IQR outlier detection on my join coverage data? I have 4 rows for group-a and group-b from 2026-10-01 to 2026-10-04 with matched_rows values of 10, 20, 15, and 30 — flag any outliers in the matched_rows field.","exampleUseCases":[{"title":"ETL pipeline join quality audit","prompt":"I want to check my ETL pipeline's join coverage for the last week — here are my daily matched_rows and source_rows for two dataset groups across 7 dates. Can you normalise the strings and flag any IQR outliers in the matched_rows column?"},{"title":"Spotting bad joins in data warehouse loads","prompt":"Something looks off in our data warehouse loads — can you run IQR outlier detection on these source_rows counts across my 12 dataset groups and also normalise the dataset_group labels? I'll send you the rows with dates and row IDs."},{"title":"Monitoring join ratios across pipeline runs","prompt":"We run daily joins across group-alpha and group-beta and want to catch unusual spikes. Please analyse these 50 join coverage records, normalise the string fields, and flag any IQR outliers in source_rows."}],"resultDescription":"Returns two analytical reports per call: (1) a string normalisation report covering the dataset_group and related string fields, and (2) an IQR outlier flag report marking which rows have statistically anomalous values in the selected numeric field (matched_rows or source_rows). No external lookups or predictions are made; all analysis is performed locally on the supplied data.","failureModes":["Fewer than 1 or more than 200 rows supplied — rejected with validation error","Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) — schema validation failure","Date not in YYYY-MM-DD format — pattern validation error","Numeric values outside allowed range (±1,000,000,000,000) — rejected","Invalid field enum value (not matched_rows or source_rows) — validation error","Insufficient data variance for meaningful IQR computation — may return degenerate outlier flags"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local statistical analysis of join coverage metrics without any external dependencies or live data lookups. It is ideal for data engineers who want to quickly audit matched_rows and source_rows for anomalies and normalise string fields in a single low-cost call. Prefer it over general-purpose analytics platforms when you want a deterministic, reproducible report on a small-to-medium batch (up to 200 rows) at minimal cost ($0.01 USDC for 2 reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:43.512Z","isFirstParty":false,"canonicalSlug":"join-coverage-analytics-string-normalisation-iqr-outlier-flags-deb6f9e4"}