{"uid":"cap_zxnUbcbKdBNC5sXpC7xG0","slug":"join-coverage-string-normalisation-numeric-interval-filter-64d44c2f","name":"Join Coverage — String Normalisation + Numeric Interval Filter","description":"Join coverage — String normalisation + Numeric interval filter: String normalisation + Numeric interval filter 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-09-026?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_mPfKB_67qTNq-y6TP33c5","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 filters join coverage records (matched_rows, source_rows) by a numeric interval, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have join coverage records for two dataset groups — take my rows (up to 200), normalise the dataset_group strings, and filter by matched_rows between 5 and 50, giving me two analytical reports.","exampleUseCases":[{"title":"Filter over-matched join rows","prompt":"I've got a list of join coverage records and I need to see only the rows where matched_rows is between 10 and 100 — can you normalise the dataset group labels and give me two reports on that filtered set?"},{"title":"Audit source_rows coverage gaps","prompt":"Run string normalisation on my dataset groups and filter out any rows where source_rows falls outside the range 0 to 500, so I can audit which join records are within a healthy coverage band."},{"title":"Data pipeline join quality check","prompt":"Before I merge these two datasets, can you take my 150 join coverage rows, normalise the group name strings, and apply a numeric interval filter on matched_rows between 1 and 200 to see which records are in range?"}],"resultDescription":"Returns two analytical reports: one covering string-normalised dataset_group values across the supplied rows, and one showing which records have the selected numeric field (matched_rows or source_rows) within the specified [minimum, maximum] interval. 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 — request rejected","row_id or dataset_group string exceeds 80 characters — validation error","date field does not match YYYY-MM-DD pattern — schema validation failure","minimum greater than maximum causing an empty interval — zero records returned in filter report","matched_rows or source_rows values outside ±1,000,000,000,000 — schema rejection","Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) — error response"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation of categorical join-coverage labels AND numeric interval filtering of coverage metrics (matched_rows or source_rows) in a single call, without requiring any external data lookups, predictions, or live enrichment. It is well-suited for data pipeline QA steps where you want to clean group name strings and simultaneously isolate records within a healthy numeric coverage band. Prefer it over general-purpose analytics tools when working with structured join coverage data up to 200 rows and needing two discrete reports for a flat $0.01 fee.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T18:54:32.005Z","isFirstParty":false,"canonicalSlug":"join-coverage-string-normalisation-numeric-interval-filter-64d44c2f"}