{"uid":"cap_FLqkhZTXr85MpZMHRwRZ_","slug":"aggregation-checks-string-normalisation-numeric-interval-filter-c18a5978","name":"Aggregation Checks — String Normalisation + Numeric Interval Filter","description":"Aggregation checks — String normalisation + Numeric interval filter: String normalisation + Numeric interval filter for supplied aggregation checks records (source_sum, aggregate_sum). 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-17-026?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","source_sum","aggregate_sum"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_sum":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"aggregate_sum":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_sum":10,"aggregate_sum":1,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","source_sum":20,"aggregate_sum":2,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","source_sum":15,"aggregate_sum":3,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","source_sum":30,"aggregate_sum":4,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["source_sum","aggregate_sum"],"type":"string","default":"source_sum"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_giICxS6Ss-KnTSms9xlWg","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 string fields and filters rows by a numeric interval for up to 200 aggregation check records (source_sum, aggregate_sum), returning 2 analytical reports.","exampleAgentPrompt":"I have 4 aggregation check rows for group-a and group-b dated 2026-10-01 through 2026-10-04 with source_sum values of 10, 20, 15, and 30 — can you normalise the strings and filter to only rows where source_sum is between 0 and 25, then give me the two analytical reports?","exampleUseCases":[{"title":"Filter out-of-range source sums","prompt":"I've got a batch of 50 aggregation check rows across dataset groups alpha and beta, with source_sum values ranging widely — can you normalise the strings and keep only the rows where source_sum falls between 5 and 500? Give me both reports."},{"title":"Validate aggregate_sum compliance","prompt":"Run string normalisation and a numeric interval filter on my 120 aggregation check records using the aggregate_sum field, flagging anything outside the range of -1000 to 1000, and return the two analytical reports."},{"title":"Daily reconciliation check on grouped data","prompt":"I need to check today's aggregation records — 80 rows grouped by region and dated 2025-07-15 — normalise the strings and filter source_sum values to only those between 0 and 100 so I can see which groups are in bounds."}],"resultDescription":"Two independent analytical reports covering: (1) string-normalised versions of the submitted row data, and (2) rows filtered to only those where the chosen numeric field (source_sum or aggregate_sum) falls within the specified minimum–maximum interval. No live lookups or external data are used; analysis is performed locally on the supplied rows.","failureModes":["Fewer than 1 or more than 200 rows submitted — request rejected","Invalid date format (not YYYY-MM-DD) — validation error","source_sum or aggregate_sum values outside ±1,000,000,000,000 — schema rejection","Missing required fields (row_id, dataset_group, date, source_sum, aggregate_sum) — validation error","minimum greater than maximum — may return empty filtered result set","Payment not completed via x402 protocol — 402 Payment Required"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, local analytical pass over up to 200 aggregation check records that combines string normalisation with numeric range filtering in a single call. It is ideal for data quality workflows where you want to clean string fields and simultaneously identify rows whose source_sum or aggregate_sum values fall outside a defined interval, without relying on any external lookup or live data source.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:54:35.110Z","isFirstParty":false,"canonicalSlug":"aggregation-checks-string-normalisation-numeric-interval-filter-c18a5978"}