{"uid":"cap_MfbmKXUFMo6vPe5N3sZg3","slug":"commercial-proposal-comparisons-record-quality-audit-string-95bda901","name":"Commercial Proposal Comparisons — Record Quality Audit + String Normalisation + Base-100 Series","description":"Commercial proposal comparisons — Record quality audit + String normalisation + Base-100 series: Record quality audit + String normalisation + Base-100 series for supplied commercial proposal comparisons records (quoted_amount, delivery_days). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-company-24-201?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","company_group","date","quoted_amount","delivery_days"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"company_group":{"type":"string","default":"","maxLength":80},"delivery_days":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"quoted_amount":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","company_group":"group-a","delivery_days":1,"quoted_amount":10},{"date":"2026-10-02","row_id":"row-2","company_group":"group-b","delivery_days":2,"quoted_amount":20},{"date":"2026-10-03","row_id":"row-3","company_group":"group-a","delivery_days":3,"quoted_amount":15},{"date":"2026-10-04","row_id":"row-4","company_group":"group-b","delivery_days":4,"quoted_amount":30}],"maxItems":200,"minItems":1},"field":{"enum":["quoted_amount","delivery_days"],"type":"string","default":"quoted_amount"},"base_index":{"type":"integer","default":0,"maximum":199,"minimum":0}}},"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_kSM_NWYOWbh5FvuKbTT_l","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":"Runs three local analytical reports on commercial proposal data (quoted_amount or delivery_days): record quality audit, string normalisation, and base-100 indexed series — up to 200 rows per call.","exampleAgentPrompt":"Can you run the record quality audit, string normalisation, and base-100 series reports on my commercial proposal data — I have 4 rows with company groups 'group-a' and 'group-b', dates from 2026-10-01 to 2026-10-04, quoted amounts of 10, 20, 15, and 30, delivery days of 1–4, and I want the analysis on the quoted_amount field indexed from row 0?","exampleUseCases":[{"title":"Vendor quote quality audit","prompt":"I have 50 supplier proposal rows with company names, dates, quoted amounts, and delivery days — can you run the three reports (quality audit, string normalisation, base-100 series) on the quoted_amount field, using the first row as the base index, so I can spot data issues and see how quotes compare?"},{"title":"Delivery days rebased comparison","prompt":"I want to compare delivery day commitments across 30 commercial proposals from different vendors — run the record quality audit, string normalisation, and base-100 series on the delivery_days field with row 0 as the base so I can see which suppliers are faster or slower relative to the baseline."},{"title":"Procurement data normalisation check","prompt":"Before I send this batch of 80 proposal comparison records to my procurement team, can you audit them for quality issues and normalise the company_group strings? Use quoted_amount as the field and index from row 0 — I need all three reports."}],"resultDescription":"Returns three independent analytical reports computed locally on the supplied rows: (1) a record quality audit flagging missing, null, or malformed values in the specified field; (2) a string normalisation report cleaning and standardising company_group and other string fields; (3) a base-100 index series rebasing the chosen numeric field (quoted_amount or delivery_days) relative to the specified base row. No external data is fetched or returned.","failureModes":["rows array empty or missing — API returns validation error","more than 200 rows submitted — rejected with maxItems error","base_index out of range for submitted row count — validation error","date strings not matching YYYY-MM-DD pattern — schema rejection","quoted_amount or delivery_days outside ±1,000,000,000,000 bounds — validation error","field enum value not 'quoted_amount' or 'delivery_days' — schema rejection","row_id missing or empty string — required field validation error"],"whenToPreferThis":"Choose this endpoint when you need three simultaneous analytical passes — quality audit, string normalisation, and base-100 indexing — on commercial proposal or vendor bid data in a single $0.01 call. It is ideal for procurement workflows where you want to validate data integrity, standardise string fields, and create a rebased numeric comparison series before further analysis. Prefer it over general-purpose analytics when your data maps cleanly to the quoted_amount/delivery_days schema and you need all three report types without external lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-17T12:43:32.144Z","isFirstParty":false,"canonicalSlug":"commercial-proposal-comparisons-record-quality-audit-string-95bda901"}