{"uid":"cap_SwNI3LpXzB9IyeMigiyJC","slug":"quality-inspections-string-normalisation-numeric-row-ordering-97196f2f","name":"Quality Inspections — String Normalisation + Numeric Row Ordering","description":"Quality inspections — String normalisation + Numeric row ordering: String normalisation + Numeric row ordering for supplied quality inspections records (inspected_items, accepted_items). 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-operations-20-025?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","team_group","date","inspected_items","accepted_items"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"team_group":{"type":"string","default":"","maxLength":80},"accepted_items":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"inspected_items":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","team_group":"group-a","accepted_items":1,"inspected_items":10},{"date":"2026-10-02","row_id":"row-2","team_group":"group-b","accepted_items":2,"inspected_items":20},{"date":"2026-10-03","row_id":"row-3","team_group":"group-a","accepted_items":3,"inspected_items":15},{"date":"2026-10-04","row_id":"row-4","team_group":"group-b","accepted_items":4,"inspected_items":30}],"maxItems":200,"minItems":1},"field":{"enum":["inspected_items","accepted_items"],"type":"string","default":"inspected_items"}}},"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_sOSsxhNgOXAc9NwgyvgAe","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 orders rows numerically for quality inspection records containing inspected_items and accepted_items, producing two local analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you run string normalisation and numeric row ordering on my quality inspection records? I have rows with team_group, date, inspected_items, and accepted_items — up to 50 rows — and I want both reports using the inspected_items field for ordering.","exampleUseCases":[{"title":"Factory floor daily QC batch analysis","prompt":"I have today's quality inspection results from our two production groups — about 30 rows with row IDs, team names, dates, and counts of inspected vs accepted items. Can you normalise the team group strings and order everything by inspected_items, and give me both reports?"},{"title":"Weekly acceptance rate data cleanup","prompt":"My weekly quality control export has messy team_group labels and I need the rows sorted by accepted_items numerically. Can you run string normalisation and numeric ordering on it — there are around 80 rows — and return the two analytical summaries?"},{"title":"Multi-team inspection comparison ordering","prompt":"We have inspection data from four different teams across the past month, about 150 rows total, with inspected_items and accepted_items counts. Please normalise the string fields and sort everything by accepted_items so I can compare team performance cleanly."}],"resultDescription":"Returns two independent analytical reports: one applying string normalisation to the team_group and other string fields across the submitted rows, and one ordering the rows numerically by the selected field (inspected_items or accepted_items). Results are computed locally against the supplied data with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, team_group, date, inspected_items, accepted_items) cause schema rejection","Invalid date format (non YYYY-MM-DD) fails pattern validation","Numeric values outside ±1,000,000,000,000 are rejected","Payment of 0.01 USDC not included causes 402 response","Empty rows array rejected by minItems:1 constraint"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local analytical processing of quality inspection records — specifically string normalisation of categorical fields and numeric ordering by inspection or acceptance counts — without requiring live data lookups, predictions, or external integrations. It is well-suited for structured QC batch data cleanup pipelines where two co-produced reports per call and a fixed low cost per batch are advantageous.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-29T12:54:01.752Z","isFirstParty":false,"canonicalSlug":"quality-inspections-string-normalisation-numeric-row-ordering-97196f2f"}