{"uid":"cap_EEUOEKO5-ZbVOQFjnRY98","slug":"competitor-supplied-prices-string-normalisation-numeric-row-ordering-33fef9ac","name":"Competitor Supplied Prices — String Normalisation + Numeric Row Ordering","description":"Competitor supplied prices — String normalisation + Numeric row ordering: String normalisation + Numeric row ordering for supplied competitor supplied prices records (list_price, package_quantity). 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-company-07-025?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","company_group","date","list_price","package_quantity"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"list_price":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"company_group":{"type":"string","default":"","maxLength":80},"package_quantity":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","list_price":10,"company_group":"group-a","package_quantity":1},{"date":"2026-10-02","row_id":"row-2","list_price":20,"company_group":"group-b","package_quantity":2},{"date":"2026-10-03","row_id":"row-3","list_price":15,"company_group":"group-a","package_quantity":3},{"date":"2026-10-04","row_id":"row-4","list_price":30,"company_group":"group-b","package_quantity":4}],"maxItems":200,"minItems":1},"field":{"enum":["list_price","package_quantity"],"type":"string","default":"list_price"}}},"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_fATdhKM4OKVWaxgiQdZ2k","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 sorts rows numerically for up to 200 competitor price records (list_price, package_quantity), returning two local analytical reports per call.","exampleAgentPrompt":"Can you normalise the string fields and sort these competitor price records numerically by list_price? I have 4 rows: row-1 for group-a on 2026-10-01 with list_price 10 and package_quantity 1, row-2 for group-b on 2026-10-02 with list_price 20 and package_quantity 2, row-3 for group-a on 2026-10-03 with list_price 15 and package_quantity 3, and row-4 for group-b on 2026-10-04 with list_price 30 and package_quantity 4.","exampleUseCases":[{"title":"Rank competitor prices for weekly review","prompt":"I've collected 50 competitor price entries across two supplier groups this week — can you normalise the company group names and sort all rows by list_price numerically so I can review them in order? Here are the rows with row IDs, dates, list prices, and package quantities."},{"title":"Clean package quantity data for comparison","prompt":"Sort these 120 competitor supplied price records by package_quantity numerically and normalise the string fields — I need two clean analytical reports back so I can compare supplier groups side by side."},{"title":"Prepare pricing dataset for downstream analysis","prompt":"Before I feed this competitor pricing data into my model, can you normalise the string columns and order the 80 rows by list_price? Use list_price as the field and give me both reports that come back."}],"resultDescription":"Two independent analytical reports produced by local engines: one covering string normalisation of fields such as company_group and date, and one covering numeric ordering of the selected field (list_price or package_quantity) across up to 200 input rows. No external lookups, predictions, or live data are included.","failureModes":["More than 200 rows submitted — request rejected with validation error","Missing required fields (row_id, company_group, date, list_price, package_quantity) — row rejected or error returned","Invalid date format (not YYYY-MM-DD) — validation failure","Numeric value outside allowed range (±1,000,000,000,000) — validation error","Empty rows array — minimum 1 item required","Invalid field enum value (not list_price or package_quantity) — schema validation error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 rows of competitor pricing data and need both string field normalisation and numeric row ordering as two bundled analytical reports in a single low-cost call ($0.01 USDC). It is best suited for offline/batch-style data cleaning and ordering tasks where no live data lookup, external enrichment, or predictive modelling is required. Prefer alternatives if you need correlation analysis, pivot tables, missing-value fill, or histogram outputs — those are separate sibling endpoints on the same platform.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T18:49:53.764Z","isFirstParty":false,"canonicalSlug":"competitor-supplied-prices-string-normalisation-numeric-row-ordering-33fef9ac"}