{"uid":"cap_gO6qlMq-3api6MmEoRivr","slug":"land-parcel-observations-record-quality-audit-string-normalisation-00d16425","name":"Land Parcel Observations — Record Quality Audit, String Normalisation & Category Frequencies","description":"Land parcel observations — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies for supplied land parcel observations records (area_m2, perimeter_m). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-weather-14-195?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_gjBLsAApokB5rFbhSMLfq","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 three analytical reports (record quality audit, string normalisation, and category frequencies) on supplied land parcel observation rows containing area and perimeter measurements.","exampleAgentPrompt":"Can you audit the quality of my land parcel records, normalise the station_group strings, and get category frequencies? Here are up to 200 rows with row_id, station_group, date, area_m2, and perimeter_m values.","exampleUseCases":[{"title":"Pre-analysis data quality check","prompt":"I have a batch of land parcel survey records with area_m2 and perimeter_m values tagged by station_group and date — can you run a quality audit on them, flag any weird or malformed entries, and tell me how often each station_group appears?"},{"title":"String normalisation for parcel database","prompt":"My parcel observation export has inconsistent station_group labels — things like 'Group A', 'group-a', 'GROUP A'. Can you normalise all those strings and also give me a frequency count of each category across my 150 rows?"},{"title":"Routine data validation before GIS import","prompt":"Before I import these land parcel records into our GIS system, can you do a record quality audit and check the area_m2 and perimeter_m columns for nulls, out-of-range values, or formatting issues across all 200 rows?"}],"resultDescription":"Three independent analytical reports: (1) a record quality audit flagging nulls, out-of-range values, and formatting issues in area_m2 and perimeter_m; (2) a string normalisation report for the station_group field; (3) a category frequency table showing how often each station_group value appears across the submitted rows.","failureModes":["Rows array is empty or missing — returns validation error","More than 200 rows submitted — exceeds maximum batch size","Required fields (row_id, station_group, date, area_m2, perimeter_m) missing from one or more rows — schema validation failure","Date field does not match YYYY-MM-DD pattern — rejected","Payment not included or insufficient — 402 payment required response"],"whenToPreferThis":"Choose this endpoint when you need three bundled analytical reports — quality audit, string normalisation, and category frequencies — on land parcel observation data (area and perimeter measurements) in a single low-cost call. It is ideal for pre-processing pipelines before GIS import or downstream analysis, where you want to catch data quality issues and understand category distributions without writing custom code. Not suitable for live lookups, predictions, or datasets exceeding 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T12:36:31.248Z","isFirstParty":false,"canonicalSlug":"land-parcel-observations-record-quality-audit-string-normalisation-00d16425"}