{"uid":"cap_fNbiWzpo2d56ENSCGPKQY","slug":"record-routing-string-normalisation-exact-row-deduplication-0e6d256f","name":"Record Routing — String Normalisation + Exact Row Deduplication","description":"Record routing — String normalisation + Exact row deduplication: String normalisation + Exact row deduplication 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-19-020?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}}},"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_cH0gmkXAvmJSCL8saTw0T","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 strings and deduplicates exact rows in record routing datasets (accepted_records, rejected_records) across up to 200 rows, producing 2 analytical reports.","exampleAgentPrompt":"Can you normalise the strings and deduplicate exact rows in my record routing data? I have 4 rows across two groups — group-a and group-b — with dates from 2026-10-01 to 2026-10-04, accepted record counts of 10, 20, 15, and 30, and rejected counts of 1, 2, 3, and 4. I need both the normalisation and deduplication reports.","exampleUseCases":[{"title":"ETL pipeline deduplication check","prompt":"Before I load today's routing batch into the warehouse, can you run string normalisation and exact row deduplication on these 50 record routing rows — they cover group-a and group-b entries from 2026-11-01 to 2026-11-20 with their accepted and rejected record counts? I want both reports so I can catch any duplicates."},{"title":"Data quality audit for routing records","prompt":"I need a data quality check on our record routing export — 120 rows with dataset groups like 'north-region' and 'south-region', dated from 2026-09-01 through 2026-09-30, each with accepted and rejected record counts. Please normalise the strings and flag any exact duplicate rows in a deduplication report."},{"title":"Pre-analysis cleanup of rejected records","prompt":"Our analyst wants to analyse rejection rates but the raw routing data has messy strings and possible duplicate rows. Can you clean up 80 rows of accepted and rejected record counts across groups 'web-channel' and 'mobile-channel' for dates in October 2026 — give me the normalisation report and the exact deduplication report?"}],"resultDescription":"Returns two analytical reports per call: (1) a string normalisation report showing standardised string fields across the submitted rows, and (2) an exact row deduplication report identifying and removing fully duplicate rows from the accepted_records and rejected_records dataset. Both reports are computed locally with no external lookups.","failureModes":["More than 200 rows submitted — request exceeds maxItems limit and will be rejected","Missing required fields (row_id, date, dataset_group, accepted_records, rejected_records) cause validation errors","Date strings not matching YYYY-MM-DD pattern are rejected","Numeric values outside ±1,000,000,000,000 range fail schema validation","Empty rows array (minItems: 1 not met) returns an error"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local string normalisation and exact row deduplication specifically on record routing data (accepted_records, rejected_records fields) in batches up to 200 rows. It is ideal for pre-ETL data cleaning, audit workflows, or quick QA passes where no external lookup, ML prediction, or live data enrichment is required. It covers both analytical tasks in a single low-cost call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:37:47.955Z","isFirstParty":false,"canonicalSlug":"record-routing-string-normalisation-exact-row-deduplication-0e6d256f"}