{"uid":"cap_WKDCreJeSi9seF1KmCS0Q","slug":"request-latency-record-quality-audit-string-normalisation-numeric-row-c5add4a7","name":"Request Latency — Record Quality Audit + String Normalisation + Numeric Row Ordering","description":"Request latency — Record quality audit + String normalisation + Numeric row ordering: Record quality audit + String normalisation + Numeric row ordering for supplied request latency records (latency_ms, request_count). 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-software-03-196?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","component","date","latency_ms","request_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"component":{"type":"string","default":"","maxLength":80},"latency_ms":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"request_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","component":"group-a","latency_ms":10,"request_count":1},{"date":"2026-10-02","row_id":"row-2","component":"group-b","latency_ms":20,"request_count":2},{"date":"2026-10-03","row_id":"row-3","component":"group-a","latency_ms":15,"request_count":3},{"date":"2026-10-04","row_id":"row-4","component":"group-b","latency_ms":30,"request_count":4}],"maxItems":200,"minItems":1},"field":{"enum":["latency_ms","request_count"],"type":"string","default":"latency_ms"}}},"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_kD9kOSBRZEoCamM3JD46d","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":"Audits record quality, normalises string fields, and orders rows numerically for up to 200 request latency records (latency_ms, request_count) in three bundled reports.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and numeric row ordering on my request latency dataset? I have rows with fields like row_id, component, date, latency_ms, and request_count — up to 50 rows — and I want the analysis focused on latency_ms.","exampleUseCases":[{"title":"Latency log cleanup before reporting","prompt":"I have a batch of 80 request latency log rows with component names, dates, latency_ms, and request_count values — can you audit their quality, normalise the component strings, and order them numerically by latency_ms so I can prepare a clean performance report?"},{"title":"Detect bad data in request count records","prompt":"Some of my request_count entries look suspicious — can you run a quality audit and string normalisation pass on my 120 latency records and order them by request_count so I can spot the outliers?"},{"title":"Pre-processing latency data for dashboarding","prompt":"Before I load my API latency data into our dashboard, I want to run it through a quality check, normalise the component labels, and get the rows ordered numerically by latency_ms — I've got about 150 rows with the standard latency_ms and request_count fields."}],"resultDescription":"Three independent analytical reports per call: (1) a record quality audit flagging issues in the supplied rows, (2) a string normalisation report on text fields like component names, and (3) a numeric row ordering report ranking rows by the selected numeric field (latency_ms or request_count). All analysis is performed locally on the supplied data with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, component, date, latency_ms, request_count) causes rejection","Invalid date format (not YYYY-MM-DD) fails schema validation","Selecting a field value other than 'latency_ms' or 'request_count' is rejected by enum constraint","Empty rows array is rejected (minItems: 1)","Payment failure via x402 protocol blocks the request"],"whenToPreferThis":"Choose this endpoint when you need three bundled data-quality operations (audit, string normalisation, row ordering) on request latency tabular data in a single low-cost call. It is ideal for pre-processing raw latency logs before dashboarding, reporting, or storage. It does not perform live monitoring, prediction, or external lookups, so prefer it for offline batch cleaning of up to 200 rows rather than real-time alerting.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:39:30.088Z","isFirstParty":false,"canonicalSlug":"request-latency-record-quality-audit-string-normalisation-numeric-row-c5add4a7"}