{"uid":"cap_rR9GUDdtL97uiLk9N8cxw","slug":"building-footprint-observations-string-normalisation-keyed-dataset-88c4728d","name":"Building Footprint Observations — String Normalisation + Keyed Dataset Comparison","description":"Building footprint observations — String normalisation + Keyed dataset comparison: String normalisation + 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-16-036?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}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"height_m":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"footprint_m2":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"station_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","height_m":1,"footprint_m2":10,"station_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","height_m":2,"footprint_m2":20,"station_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","height_m":3,"footprint_m2":15,"station_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","height_m":4,"footprint_m2":30,"station_group":"group-b"}],"maxItems":200,"minItems":1},"other":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","footprint_m2","height_m"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","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}}},"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_LDfO6C9LGvrWPseXxSUtO","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 strings and performs keyed dataset comparison on up to 200 building footprint observation records (footprint_m2, height_m), returning 2 analytical reports.","exampleAgentPrompt":"I have two batches of building footprint observations — each with up to 200 rows containing row_id, station_group, date, footprint_m2, and height_m — can you run string normalisation and a keyed comparison across both datasets and give me the 2 reports?","exampleUseCases":[{"title":"Cross-source footprint data reconciliation","prompt":"I've got two exports of building footprint measurements from different surveyors for the same sites — rows with row_id, station_group, date, footprint_m2, and height_m — can you normalise the strings and compare them side by side to find where the values differ?"},{"title":"Station group naming audit","prompt":"Our building observation records have messy station_group labels across two datasets — run string normalisation on both and compare them by row_id so we can see which groups have inconsistent naming or mismatched footprint and height values."},{"title":"Before-and-after renovation comparison","prompt":"I have pre-renovation and post-renovation building footprint records for about 150 buildings, each with a row_id, date, footprint_m2, and height_m — please compare the two datasets by key and normalise any string fields so I can see what changed."}],"resultDescription":"Two analytical reports: (1) a string normalisation report covering station_group and other text fields across the submitted rows, and (2) a keyed dataset comparison report showing value-level differences between the 'rows' and 'other' input arrays matched by row_id. No live data, predictions, or external lookups are included.","failureModes":["Input arrays exceed 200 rows — request will be rejected","Missing required fields (row_id, station_group, date, footprint_m2, height_m) cause validation errors","Date strings not matching YYYY-MM-DD pattern are rejected","Numeric values outside the allowed range cause schema validation failure","Empty rows array returns an error (minItems: 1)","Mismatched row_ids between 'rows' and 'other' may result in incomplete comparison output"],"whenToPreferThis":"Choose this endpoint when you have two batches of building footprint observation records (up to 200 rows each) and need both string normalisation and a keyed row-by-row comparison in a single low-cost call. It is ideal for data reconciliation, survey auditing, and detecting discrepancies between two structured building datasets without needing live lookups or external data enrichment.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-30T07:09:22.212Z","isFirstParty":false,"canonicalSlug":"building-footprint-observations-string-normalisation-keyed-dataset-88c4728d"}