{"uid":"cap_MtXi6g2GkuLcDkYr8Vh8_","slug":"record-routing-string-normalisation-adjacent-row-changes-14b0aab4","name":"Record Routing — String Normalisation + Adjacent Row Changes","description":"Record routing — String normalisation + Adjacent row changes: String normalisation + Adjacent row changes for supplied record routing records (accepted_records, rejected_records). 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-analytics-19-029?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","accepted_records","rejected_records"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"accepted_records":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"rejected_records":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","accepted_records":10,"rejected_records":1},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","accepted_records":20,"rejected_records":2},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","accepted_records":15,"rejected_records":3},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","accepted_records":30,"rejected_records":4}],"maxItems":200,"minItems":1},"field":{"enum":["accepted_records","rejected_records"],"type":"string","default":"accepted_records"}}},"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_OQisk6wc7m0WkZLjQJ9s6","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 two analytical reports on record routing data: string normalisation and adjacent row change detection for accepted/rejected record counts across up to 200 rows.","exampleAgentPrompt":"Can you run string normalisation and adjacent row change analysis on my record routing data? I have rows for group-a and group-b from 2026-10-01 to 2026-10-04, with accepted_records values of 10, 20, 15, 30 and rejected_records of 1, 2, 3, 4 — analyse the accepted_records field.","exampleUseCases":[{"title":"Routing pipeline quality check","prompt":"I've got four days of routing data across two dataset groups with accepted and rejected record counts — can you normalise the strings and show me how each row's accepted_records changed compared to the previous one?"},{"title":"Rejection spike investigation","prompt":"Analyse my record routing dataset for adjacent row changes in rejected_records — I have 10 rows from 2026-11-01 to 2026-11-10 across group-a and group-b so I can see where rejection counts jumped."},{"title":"Multi-group routing trend review","prompt":"Run both the string normalisation and adjacent row change reports on my record routing data — I have 20 rows split across three dataset groups covering October, and I want to focus on the accepted_records field."}],"resultDescription":"Two analytical reports covering: (1) string normalisation of routing record fields, and (2) adjacent row change detection showing how accepted_records or rejected_records values shift between consecutive rows. Results are computed locally from the supplied data with no external lookups.","failureModes":["Rows array is empty or exceeds 200 items — validation error","Invalid date format not matching YYYY-MM-DD pattern — schema rejection","Numeric values outside ±1,000,000,000,000 range — schema rejection","Missing required fields (row_id, date, accepted_records, rejected_records) — validation error","Invalid field enum value other than accepted_records or rejected_records — schema rejection"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation and adjacent row change analysis on record routing data in a single low-cost call. It is ideal for data pipeline monitoring, ETL quality checks, and routing acceptance/rejection trend analysis on batches up to 200 rows. It does not perform live lookups or predictive modelling — use it for deterministic local computation on supplied tabular data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:54:37.360Z","isFirstParty":false,"canonicalSlug":"record-routing-string-normalisation-adjacent-row-changes-14b0aab4"}