{"uid":"cap_D5h2Wd6XS1vyOTNtk2e7L","slug":"feature-availability-analytics-numeric-interval-filter-daily-sum-series-d1fb6e72","name":"Feature Availability Analytics — Numeric Interval Filter + Daily Sum Series","description":"Feature availability — Numeric interval filter + Daily sum series: Numeric interval filter + Daily sum series 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-10-125?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_LCo2dGn22Xx8UKojipPza","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 local analytical reports on feature availability data: filters rows by a numeric interval and produces a daily sum time series for populated_features or expected_features.","exampleAgentPrompt":"Can you filter our feature availability records where populated_features is between 0 and 100, and also give me the daily sum series for populated_features? Here are the rows: row-1 on 2026-10-01 group-a with expected=1 populated=10, row-2 on 2026-10-02 group-b expected=2 populated=20, row-3 on 2026-10-03 group-a expected=3 populated=15.","exampleUseCases":[{"title":"Daily feature completeness trend check","prompt":"I have feature availability logs for October — each row has a date, dataset group, and counts of populated vs expected features. Can you filter rows where populated_features is between 5 and 30 and show me the daily sum series for populated_features? Rows: row-1 2026-10-01 group-a expected=10 populated=8, row-2 2026-10-02 group-b expected=20 populated=25, row-3 2026-10-03 group-a expected=15 populated=12."},{"title":"Expected features interval audit","prompt":"Run an interval filter on expected_features between 2 and 50 and give me the daily totals as a time series. My data: row-1 on 2026-10-01 group-a expected=5 populated=10, row-2 on 2026-10-02 group-b expected=45 populated=60, row-3 on 2026-10-03 group-c expected=100 populated=90."},{"title":"Cross-group feature gap monitoring","prompt":"I want to see which days and groups fall within my acceptable feature range — filter rows where populated_features is between 15 and 50, and give me a daily sum series too. Here's my data: row-1 2026-10-01 group-alpha expected=20 populated=18, row-2 2026-10-02 group-beta expected=30 populated=45, row-3 2026-10-03 group-alpha expected=25 populated=22, row-4 2026-10-04 group-beta expected=10 populated=50."}],"resultDescription":"Returns two analytical reports: (1) a numeric interval filter report listing rows where the selected field falls within the specified minimum–maximum range, and (2) a daily sum time series aggregating the selected field (populated_features or expected_features) by date. Up to 200 input rows supported per call.","failureModes":["Input array is empty or exceeds 200 rows — validation error returned","Date field does not match YYYY-MM-DD pattern — row rejected","populated_features or expected_features values outside ±1,000,000,000,000 — schema violation","minimum greater than maximum — may return empty filter results with no error","Missing required fields (row_id, date) — request rejected","Payment of 0.01 USDC not completed — 402 response with payment details"],"whenToPreferThis":"Choose this endpoint when you need to perform both an interval-based filter AND a daily sum aggregation on feature availability data (populated vs expected features) in a single paid call. It is best suited for data quality monitoring workflows where you want to identify out-of-range feature counts and understand day-by-day totals without connecting to an external database or running your own analytics engine. Not suitable for real-time lookups, predictions, or datasets larger than 200 rows per call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:19:39.963Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-numeric-interval-filter-daily-sum-series-d1fb6e72"}