{"uid":"cap_DrpTMoujyec1HvcY1n3uE","slug":"building-footprint-observations-numeric-interval-filter-keyed-dataset-186f1db7","name":"Building Footprint Observations — Numeric Interval Filter + Keyed Dataset Comparison","description":"Building footprint observations — Numeric interval filter + Keyed dataset comparison: Numeric interval filter + Keyed dataset comparison for supplied building footprint observations records (footprint_m2, height_m). 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-weather-16-134?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","footprint_m2","height_m"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"height_m":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"footprint_m2":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"station_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","height_m":1,"footprint_m2":10,"station_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","height_m":2,"footprint_m2":20,"station_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","height_m":3,"footprint_m2":15,"station_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","height_m":4,"footprint_m2":30,"station_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["footprint_m2","height_m"],"type":"string","default":"footprint_m2"},"other":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","footprint_m2","height_m"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"height_m":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"footprint_m2":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"station_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","height_m":1,"footprint_m2":10,"station_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","height_m":2,"footprint_m2":21,"station_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","height_m":3,"footprint_m2":17,"station_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","height_m":4,"footprint_m2":33,"station_group":"group-b"}],"maxItems":200,"minItems":0},"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_-V6uN3tO72vbyaY7ZVnU6","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":"Applies a numeric interval filter and keyed dataset comparison to two supplied sets of building footprint observation records (footprint_m2, height_m), returning 2 analytical reports for up to 200 rows.","exampleAgentPrompt":"I have two sets of building footprint observation records — a primary dataset and a comparison dataset, each up to 200 rows with row_id, station_group, date, footprint_m2, and height_m — can you run a numeric interval filter and keyed dataset comparison on the footprint_m2 field and give me both reports?","exampleUseCases":[{"title":"Compare building footprint datasets across survey periods","prompt":"I have two batches of building footprint survey records from October 2026 — the original readings and a revised set — each with row_id, station_group, date, footprint_m2, and height_m. Can you run a keyed dataset comparison and numeric interval filter on footprint_m2 to show me where the two datasets differ and which records fall within specific size ranges?"},{"title":"Filter buildings by height within operational range","prompt":"I've got a list of building observations with height_m values and I need to filter them by a numeric interval on height_m, plus compare them against a second dataset of the same buildings from a different source. Can you run both reports on height_m for up to 200 rows so I can see which buildings meet the height criteria and how the two datasets diverge?"},{"title":"Audit two building footprint record batches for discrepancies","prompt":"We collected building footprint measurements from two different teams — both datasets have row_id, station_group, date, footprint_m2, and height_m fields for the same buildings. Please do a keyed comparison and numeric interval filter on footprint_m2 across both batches so I can audit where the teams recorded different values."}],"resultDescription":"Returns 2 analytical reports: (1) a numeric interval filter report showing which records from the primary dataset fall within specified value ranges for the chosen field (footprint_m2 or height_m), and (2) a keyed dataset comparison report highlighting differences between the primary and secondary datasets matched by row_id. Both reports are computed locally from supplied data with no external lookups.","failureModes":["Empty rows array or missing required row fields (row_id, station_group, date, footprint_m2, height_m) causes validation error","Supplying more than 200 rows exceeds the maximum and will be rejected","Invalid date format (not YYYY-MM-DD) causes schema validation failure","Null numeric values for the selected field may affect interval filter accuracy","Missing or mismatched row_ids between primary and comparison datasets reduces keyed comparison coverage","Payment failure or insufficient USDC balance prevents request processing"],"whenToPreferThis":"Choose this endpoint when you have two structured building footprint observation datasets and need both a numeric range filter and a keyed cross-dataset comparison in a single call. It is ideal for auditing measurement discrepancies between survey teams, comparing building records across time periods, or validating that footprint_m2 or height_m values fall within acceptable bounds — all without sending data to external services, since all analytics run on locally supplied data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-22T12:45:52.819Z","isFirstParty":false,"canonicalSlug":"building-footprint-observations-numeric-interval-filter-keyed-dataset-186f1db7"}