{"uid":"cap_5wwtdBD43K4wpCwEWae0g","slug":"land-parcel-observations-record-quality-audit-string-normalisation-b237f6f2","name":"Land Parcel Observations — Record Quality Audit + String Normalisation","description":"Land parcel observations — Record quality audit + String normalisation: Record quality audit + String normalisation for supplied land parcel observations records (area_m2, perimeter_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-14-001?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","area_m2","perimeter_m"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"area_m2":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"perimeter_m":{"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","area_m2":10,"perimeter_m":1,"station_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","area_m2":20,"perimeter_m":2,"station_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","area_m2":15,"perimeter_m":3,"station_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","area_m2":30,"perimeter_m":4,"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_W4KS8YTE1hECzvlRRaJsj","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":"Audits data quality and normalises strings for land parcel observation records (area_m2, perimeter_m) across up to 200 rows, returning two independent analytical reports.","exampleAgentPrompt":"Can you run a record quality audit and string normalisation on these land parcel observations? I have up to 50 rows, each with a row_id, station_group, date, area_m2, and perimeter_m — I need both reports back.","exampleUseCases":[{"title":"Pre-ingestion parcel data cleanup","prompt":"Before I load this batch of 120 land parcel records into our database, can you audit them for quality issues and normalise the station_group strings? Each row has a row_id, date, area_m2, and perimeter_m."},{"title":"Detecting bad measurements in survey data","prompt":"I've got 80 rows of land parcel survey observations with area and perimeter values — some look suspicious. Can you run a quality audit to flag any problematic records and normalise the string fields?"},{"title":"Validating parcel data before GIS import","prompt":"I need to validate these 200 land parcel observation rows before importing them into our GIS system. Please run the record quality audit and string normalisation reports on the area_m2, perimeter_m, station_group, and date fields."}],"resultDescription":"Two independent analytical reports: (1) a record quality audit identifying issues such as nulls, out-of-range values, and inconsistencies in area_m2 and perimeter_m fields; (2) a string normalisation report covering fields like station_group and date, all computed locally without any external lookups.","failureModes":["Submitting more than 200 rows returns a validation error","Missing required fields (row_id, station_group, date, area_m2, perimeter_m) causes row rejection","Invalid date format (not YYYY-MM-DD) triggers a schema error","row_id or station_group strings exceeding 80 characters are rejected","Empty rows array returns an error","Payment failure via x402 protocol prevents processing"],"whenToPreferThis":"Choose this endpoint when you need offline, local analytical auditing of land parcel observation records (area_m2, perimeter_m) without any external lookups or predictions. It is ideal for batches up to 200 rows where you want both a record quality audit and string normalisation in a single low-cost call ($0.01 USDC). Prefer it over generic validation services when the data specifically involves land parcel measurements with station_group groupings.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-30T19:10:12.341Z","isFirstParty":false,"canonicalSlug":"land-parcel-observations-record-quality-audit-string-normalisation-b237f6f2"}