{"uid":"cap_vjrWU6Q8hSFL0PlLtJ6_S","slug":"building-footprint-observations-record-quality-audit-string-9d322a3d","name":"Building Footprint Observations — Record Quality Audit & String Normalisation","description":"Building footprint observations — Record quality audit + String normalisation: Record quality audit + String normalisation 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-16-001?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}}},"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_xoL3X_Lo5aMybC5N57vVJ","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 a record quality audit and string normalisation on up to 200 building footprint observation rows (footprint_m2, height_m), returning two independent analytical reports.","exampleAgentPrompt":"Can you audit the quality of my building footprint observation records and normalise the string fields? I have about 50 rows with columns for row_id, station_group, date, footprint_m2, and height_m — I need both the record quality audit and the string normalisation reports.","exampleUseCases":[{"title":"Pre-analysis data quality check","prompt":"I've got 120 building footprint observation rows with footprint_m2 and height_m values and I need to audit them for quality issues before I run my analysis — can you produce both the record quality audit and the string normalisation report for all of them?"},{"title":"Cleaning building survey dataset","prompt":"My team collected building survey data with station_group labels and footprint measurements across 80 sites — can you normalise the station_group strings and flag any record quality problems in the footprint_m2 and height_m columns?"},{"title":"Validating imported construction records","prompt":"I just imported 200 rows of building height and footprint observations from a third-party source and I'm worried about inconsistent strings and bad data — please run a full record quality audit and string normalisation on all of them."}],"resultDescription":"Two independent analytical reports per batch: (1) a record quality audit highlighting issues such as missing values, out-of-range measurements, and malformed dates across the supplied footprint_m2 and height_m fields; and (2) a string normalisation report showing standardised versions of string fields like station_group. Both reports cover up to 200 input rows processed locally without external lookups.","failureModes":["Exceeding 200 rows causes a validation error rejecting the request","Missing required fields (row_id, station_group, date, footprint_m2, height_m) on any row causes rejection","Invalid date format (not YYYY-MM-DD) triggers a schema validation failure","row_id exceeding 80 characters or being empty causes a validation error","Numeric values outside the allowed range cause schema rejection","Malformed JSON body returns an error response"],"whenToPreferThis":"Choose this endpoint when you have up to 200 building footprint observation records (with footprint_m2 and height_m fields) and need both a data quality audit and string normalisation in a single call, without any live lookup, prediction, or external data enrichment. It is ideal for pre-processing pipelines that need to validate and clean building measurement data locally before downstream analysis.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T12:33:00.318Z","isFirstParty":false,"canonicalSlug":"building-footprint-observations-record-quality-audit-string-9d322a3d"}