{"uid":"cap_XJhNciFZT1yidnbxy8DPp","slug":"financial-statement-inputs-string-normalisation-date-by-category-pivot-eae50750","name":"Financial Statement Inputs — String Normalisation + Date by Category Pivot","description":"Financial statement inputs — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied financial statement inputs records (revenue_amount, expense_amount). 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-finance-22-035?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","instrument_group","date","revenue_amount","expense_amount"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"expense_amount":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"revenue_amount":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"instrument_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","expense_amount":1,"revenue_amount":10,"instrument_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","expense_amount":2,"revenue_amount":20,"instrument_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","expense_amount":3,"revenue_amount":15,"instrument_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","expense_amount":4,"revenue_amount":30,"instrument_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["revenue_amount","expense_amount"],"type":"string","default":"revenue_amount"}}},"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_pWYMpo9NO1vz97i7j5gxv","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 string fields and produces a date-by-category pivot table from up to 200 rows of financial statement data (revenue and expense amounts).","exampleAgentPrompt":"Take these 4 rows of financial statement data — row-1 on 2026-10-01 with revenue 10 and expense 1 for group-a, row-2 on 2026-10-02 with revenue 20 and expense 2 for group-b, row-3 on 2026-10-03 with revenue 15 and expense 3 for group-a, row-4 on 2026-10-04 with revenue 30 and expense 4 for group-b — run string normalisation on the text fields and give me a date-by-category pivot on revenue_amount.","exampleUseCases":[{"title":"Monthly revenue pivot by instrument group","prompt":"I have 150 rows of monthly financial statement entries with revenue and expense amounts across several instrument groups — can you normalise the string fields and pivot the revenue_amount by date and category so I can see totals at a glance?"},{"title":"Expense data cleanup and pivot","prompt":"My expense records for the last quarter are messy with inconsistent instrument group labels. Can you normalise those strings and then give me a date-by-category pivot on the expense_amount field across all 80 rows?"},{"title":"Dual-report financial data audit","prompt":"I've got two sets of financial rows — each up to 100 rows — with revenue and expense amounts. Can you run string normalisation and the date-by-category pivot for both sets in one call so I get two clean reports back?"}],"resultDescription":"Returns two analytical reports for the submitted rows: (1) a string-normalised version of the text fields (row_id, instrument_group) and (2) a date-by-category pivot table for the selected numeric field (revenue_amount or expense_amount), aggregated by date and instrument_group across up to 200 input rows.","failureModes":["Fewer than 1 row or more than 200 rows submitted — validation error","Invalid date format (not YYYY-MM-DD) — schema validation failure","revenue_amount or expense_amount outside ±1,000,000,000,000 range — rejection","Missing required fields (row_id, instrument_group, date) — schema error","Invalid field enum value (not revenue_amount or expense_amount) — rejection","Payment not included or insufficient — 402 Payment Required"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation of text identifiers and a date-by-category pivot table in a single low-cost call on financial statement data (revenue and expense amounts). It is well-suited for batch processing up to 200 rows of structured financial records locally without external lookups or predictions, and delivers two distinct analytical reports for $0.01 USDC per call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-30T12:36:49.650Z","isFirstParty":false,"canonicalSlug":"financial-statement-inputs-string-normalisation-date-by-category-pivot-eae50750"}