{"uid":"cap_HwLZ4_sgJvqmrrFQP421d","slug":"field-survey-coverage-string-normalisation-date-by-category-pivot-8219478e","name":"Field Survey Coverage String Normalisation + Date by Category Pivot","description":"Field survey coverage — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied field survey coverage records (surveyed_m2, target_m2). 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-22-035?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","surveyed_m2","target_m2"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"target_m2":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"surveyed_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","target_m2":1,"surveyed_m2":10,"station_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","target_m2":2,"surveyed_m2":20,"station_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","target_m2":3,"surveyed_m2":15,"station_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","target_m2":4,"surveyed_m2":30,"station_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["surveyed_m2","target_m2"],"type":"string","default":"surveyed_m2"}}},"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_MviM9DqFHSwoDoqoCFgHZ","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 station group strings and produces a date-by-category pivot table from field survey coverage records (surveyed_m2, target_m2) — two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have 4 rows of field survey coverage data with station groups like 'group-a' and 'group-b', dates from 2026-10-01 to 2026-10-04, and surveyed_m2 values — can you run string normalisation and a date-by-category pivot on the surveyed_m2 field for me?","exampleUseCases":[{"title":"Environmental survey coverage audit","prompt":"I've got 150 rows of field survey records with station groups, dates, and surveyed_m2 values — some group names have inconsistent capitalisation. Can you normalise the strings and give me a pivot table showing surveyed_m2 by date and station group?"},{"title":"Target vs surveyed area comparison pivot","prompt":"Here are 80 field survey records with row IDs, station groups, dates, and both target_m2 and surveyed_m2 columns — run the string normalisation and date-by-category pivot on the target_m2 field so I can see how targets break down across groups and dates."},{"title":"Monthly survey reporting for habitat monitoring","prompt":"I need two reports on my habitat survey data: normalise the station group labels and pivot the surveyed_m2 figures by date and category. I have 60 rows with dates in YYYY-MM-DD format and groups named things like 'north-zone' and 'south-zone'."}],"resultDescription":"Returns two analytical reports: (1) a string-normalised version of the station_group field across all submitted rows, and (2) a date-by-category pivot table aggregating the selected metric (surveyed_m2 or target_m2) across dates and station groups — all computed locally with no external lookups.","failureModes":["Rows exceeding 200 items will be rejected (maxItems constraint)","Missing required fields (row_id, station_group, date, surveyed_m2, target_m2) will cause validation errors","Dates not matching YYYY-MM-DD pattern will fail schema validation","Numeric values outside ±1,000,000,000,000 range will be rejected","Invalid field enum value (must be 'surveyed_m2' or 'target_m2') will cause an error","Payment of 0.01 USDC must be included via x402 protocol or the request will be rejected"],"whenToPreferThis":"Choose this endpoint when you have field survey coverage records with station groups and need both string normalisation of category labels and a date-by-category pivot in a single call. It is ideal for up to 200 rows of survey data where you need two bundled analytical outputs at low cost ($0.01 USDC), without requiring live data lookups or external integrations.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T12:33:56.898Z","isFirstParty":false,"canonicalSlug":"field-survey-coverage-string-normalisation-date-by-category-pivot-8219478e"}