{"uid":"cap_23TgqDVWShg0CsyGC-m0u","slug":"feature-availability-analytics-numeric-distribution-interval-filter-5aaabee9","name":"Feature Availability Analytics — Numeric Distribution & Interval Filter","description":"Feature availability — Numeric distribution + Numeric interval filter: Numeric distribution + Numeric interval filter 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-10-074?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_qihJXimrl8F_6vx4Ihrtq","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 a numeric distribution and numeric interval filter report for up to 200 feature availability records (populated_features, expected_features), returning 2 analytical reports per call.","exampleAgentPrompt":"Analyze the distribution of populated_features in my dataset and filter results between 5 and 50 — here are up to 200 rows with row_id, date, dataset_group, populated_features, and expected_features values. Give me the numeric distribution and interval filter reports.","exampleUseCases":[{"title":"Dataset feature coverage audit","prompt":"I have 150 daily records tracking how many features are populated vs expected across two dataset groups. Can you run a numeric distribution on populated_features and filter to rows where the value falls between 10 and 100, so I can see which dates and groups have good coverage?"},{"title":"ML pipeline completeness check","prompt":"Before training, I want to understand the spread of expected_features across my pipeline rows — can you generate a distribution report and also show me only the records where expected_features is between 0 and 500? I'll send you the batch of up to 200 rows."},{"title":"Feature gap interval analysis","prompt":"I've got a CSV of 80 records with populated_features and expected_features per date and group. Run the numeric distribution and interval filter with a minimum of 0 and maximum of 200 on the expected_features field so I can find which records are within an acceptable range."}],"resultDescription":"Two analytical reports: (1) a numeric distribution summarizing how values of the selected field (populated_features or expected_features) are spread across the supplied rows, and (2) an interval filter report identifying which of the up to 200 rows fall within the specified minimum–maximum numeric range.","failureModes":["Input array is empty or exceeds 200 rows — validation error returned","Invalid date format in any row (must be YYYY-MM-DD) — schema validation failure","minimum value greater than maximum value — may return empty interval filter results","Null values in populated_features or expected_features may be excluded from distribution calculations","Payment of 0.01 USDC not completed — 402 Payment Required response"],"whenToPreferThis":"Choose this endpoint when you have up to 200 pre-loaded feature availability records and need both a distribution breakdown and an interval-filtered subset in a single paid call. It is ideal for lightweight, local analytical computation on feature completeness data without needing an external BI tool, database query engine, or live data connection. At $0.01 USDC for two reports, it is cost-effective for small-to-medium batch analytics on ML pipeline or data quality metrics.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:16:10.978Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-numeric-distribution-interval-filter-5aaabee9"}