{"uid":"cap_WJY6Ihkl8waxL95zgHiYR","slug":"record-routing-analytics-string-normalisation-date-by-category-pivot-9132cd00","name":"Record Routing Analytics: String Normalisation + Date by Category Pivot","description":"Record routing — String normalisation + Date by category pivot: String normalisation + Date by category pivot 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-19-035?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_zMC5uGobs2gEfSmoL5KkH","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":"Performs string normalisation and date-by-category pivot analysis on supplied record routing data (accepted_records, rejected_records) for up to 200 rows, returning two independent analytical reports.","exampleAgentPrompt":"Can you run string normalisation and a date-by-category pivot on my record routing data? I have rows with dataset_group, date, accepted_records, and rejected_records — up to 50 rows — and I want to analyze the accepted_records field.","exampleUseCases":[{"title":"ETL pipeline data quality review","prompt":"I have my ETL pipeline's record routing logs for last month — each row has a date, a dataset group, and counts of accepted and rejected records. Can you normalize the strings and pivot accepted_records by date and category so I can see which groups had the most failures?"},{"title":"Rejected records trend by group","prompt":"Here's my record routing dataset with about 80 rows — row IDs, dates, dataset groups, and accepted and rejected record counts. Run the string normalisation and date-by-category pivot on the rejected_records field so I can spot which groups are consistently producing rejections."},{"title":"Multi-group routing acceptance audit","prompt":"I want to audit our routing acceptance rates across four dataset groups over Q3. I've got 120 rows with dates, group names, accepted_records, and rejected_records. Please normalise the strings and give me a pivot of accepted_records broken down by date and category."}],"resultDescription":"Returns two independent analytical reports: (1) a string normalisation report cleaning and standardising text fields in the supplied routing records, and (2) a date-by-category pivot table summarising the selected metric (accepted_records or rejected_records) broken down by date and dataset_group — all computed locally from the supplied data with no external lookups.","failureModes":["Input array exceeds 200 rows — request will be rejected or truncated","Missing required fields (row_id, date, accepted_records, rejected_records) — validation error","Date field not in YYYY-MM-DD format — schema validation failure","Numeric values outside allowed range (-1e12 to 1e12) — validation error","Empty rows array — minimum 1 item required","Invalid field selector not in enum [accepted_records, rejected_records] — validation error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 rows of record routing data with accepted/rejected record counts grouped by category and date, and you need both string normalisation and a date-by-category pivot in a single low-cost call ($0.01 USDC). It is ideal for lightweight, local analytical processing of ETL or data pipeline quality metrics without requiring external data sources, live lookups, or prediction services.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:54:05.318Z","isFirstParty":false,"canonicalSlug":"record-routing-analytics-string-normalisation-date-by-category-pivot-9132cd00"}