{"uid":"cap_mMp36B-kw13zK4ujFR8ii","slug":"agricultural-output-string-normalisation-and-numeric-row-ordering-bef34630","name":"Agricultural Output String Normalisation and Numeric Row Ordering","description":"Agricultural output — String normalisation + Numeric row ordering: String normalisation + Numeric row ordering for supplied agricultural output records (output_tonnes, cultivated_hectares). 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-economics-16-025?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","region","date","output_tonnes","cultivated_hectares"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"region":{"type":"string","default":"","maxLength":80},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"output_tonnes":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"cultivated_hectares":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","region":"group-a","row_id":"row-1","output_tonnes":10,"cultivated_hectares":1},{"date":"2026-10-02","region":"group-b","row_id":"row-2","output_tonnes":20,"cultivated_hectares":2},{"date":"2026-10-03","region":"group-a","row_id":"row-3","output_tonnes":15,"cultivated_hectares":3},{"date":"2026-10-04","region":"group-b","row_id":"row-4","output_tonnes":30,"cultivated_hectares":4}],"maxItems":200,"minItems":1},"field":{"enum":["output_tonnes","cultivated_hectares"],"type":"string","default":"output_tonnes"}}},"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_eOLP4rD6hDvzAYZ-vWR3M","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 region/date strings and sorts agricultural output records (output_tonnes, cultivated_hectares) numerically for up to 200 rows, producing 2 local analytical reports per call.","exampleAgentPrompt":"Can you normalize the region strings and sort these agricultural rows numerically by output_tonnes? I have 4 records: row-1 (group-a, 2026-10-01, 10 tonnes, 1 ha), row-2 (group-b, 2026-10-02, 20 tonnes, 2 ha), row-3 (group-a, 2026-10-03, 15 tonnes, 3 ha), row-4 (group-b, 2026-10-04, 30 tonnes, 4 ha) — I want both the string normalisation and numeric ordering reports.","exampleUseCases":[{"title":"Ranking crop yields by production volume","prompt":"I have a set of regional crop production records with output in tonnes and area in hectares — can you normalize the region name strings and sort all rows numerically by output_tonnes so I can see which regions produced the most?"},{"title":"Cleaning inconsistent region labels before analysis","prompt":"My agricultural dataset has messy region strings like 'Group A', 'group-a', and 'GROUP A' mixed together — please normalize them and order the rows by cultivated_hectares numerically, and give me both reports."},{"title":"Pre-processing farm data for downstream reporting","prompt":"Before I hand off this batch of 150 farm output rows to my reporting pipeline, can you run string normalisation on the region and date fields and apply numeric ordering on output_tonnes to give me clean, ranked data?"}],"resultDescription":"Two analytical reports per call: one covering string normalisation of region/date fields across up to 200 input rows, and one covering numeric row ordering by the selected field (output_tonnes or cultivated_hectares), all computed locally without external lookups.","failureModes":["More than 200 rows submitted — request rejected with validation error","Missing required fields (row_id, region, date, output_tonnes, cultivated_hectares) — schema validation failure","Invalid date format (not YYYY-MM-DD) — pattern validation error","Field selector not one of the allowed enum values — validation error","Numeric values outside allowed range (±1,000,000,000,000) — validation failure"],"whenToPreferThis":"Choose this endpoint when you need deterministic, offline string normalisation and numeric sorting of agricultural records (output_tonnes, cultivated_hectares) without any live data lookup, prediction, or external enrichment. Ideal for pre-processing small-to-medium batches (up to 200 rows) of crop production data cheaply ($0.01 per call covering 2 reports) before feeding into downstream analytics or reporting pipelines.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T12:49:27.957Z","isFirstParty":false,"canonicalSlug":"agricultural-output-string-normalisation-and-numeric-row-ordering-bef34630"}