{"uid":"cap_f4sVcwai_NWio5QyQAztc","slug":"feature-availability-analytics-numeric-interval-filter-two-measure-ccfd6692","name":"Feature Availability Analytics: Numeric Interval Filter + Two-Measure Correlation","description":"Feature availability — Numeric interval filter + Two-measure correlation: Numeric interval filter + Two-measure correlation for supplied feature availability records (populated_features, expected_features). 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-10-131?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","populated_features","expected_features"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"expected_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"populated_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","expected_features":1,"populated_features":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","expected_features":2,"populated_features":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_features":3,"populated_features":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_features":4,"populated_features":30}],"maxItems":200,"minItems":1},"field":{"enum":["populated_features","expected_features"],"type":"string","default":"populated_features"},"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_vgMwxlhOfsn_R4bIWZ-oW","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":"Runs two analytical reports on feature availability data — a numeric interval filter on populated_features or expected_features, plus a Pearson/correlation analysis between the two measures — for up to 200 supplied rows.","exampleAgentPrompt":"I have 150 rows of daily feature availability records with populated_features and expected_features columns — can you run an interval filter keeping only rows where populated_features is between 5 and 50, and also compute the correlation between populated_features and expected_features across all rows?","exampleUseCases":[{"title":"Daily feature completeness audit","prompt":"Here are 80 rows of daily feature availability data with populated_features and expected_features. Filter to only rows where expected_features falls between 10 and 200, and also give me the correlation between populated_features and expected_features so I can see if they move together."},{"title":"Dataset group coverage correlation check","prompt":"I have feature availability records for two dataset groups across the past quarter — can you filter for rows where populated_features is between 0 and 100, and run a two-measure correlation between populated_features and expected_features for me?"},{"title":"Outlier band removal and feature measure relationship","prompt":"I've got 200 rows of feature availability data. Please filter out any rows where populated_features is outside the range 20 to 500, and compute how strongly populated_features and expected_features are correlated across the full dataset."}],"resultDescription":"Returns two independent analytical reports: (1) a list of rows from the supplied data whose chosen numeric field (populated_features or expected_features) falls within the specified [minimum, maximum] interval, and (2) a statistical correlation report quantifying the relationship between populated_features and expected_features across all supplied rows. Both reports are computed locally with no external lookups.","failureModes":["Fewer than 1 row supplied — minItems constraint violation","More than 200 rows supplied — maxItems constraint violation","Invalid date format (must be YYYY-MM-DD) — schema pattern validation error","Numeric values outside ±1,000,000,000,000 range — schema minimum/maximum violation","Missing required fields (row_id, date, populated_features, expected_features) — schema required field error","Minimum greater than maximum — may return empty filter result set","Null values in both measures — correlation report may be undefined or return NaN"],"whenToPreferThis":"Choose this endpoint when you have up to 200 rows of feature availability data with populated_features and expected_features columns and need both a numeric range filter and a two-measure statistical correlation in a single low-cost call. It is self-contained with no external data lookups, making it ideal for batch analytical jobs on supplied datasets. Prefer alternatives if you need time-series forecasting, more than 200 rows, or more than two measures correlated simultaneously.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:19:42.463Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-numeric-interval-filter-two-measure-ccfd6692"}