{"uid":"cap_uSKSDhZTRt8FceXR2NPhF","slug":"expense-claims-numeric-interval-filter-and-iqr-outlier-flags-5e8b607d","name":"Expense Claims Numeric Interval Filter and IQR Outlier Flags","description":"Expense claims — Numeric interval filter + IQR outlier flags: Numeric interval filter + IQR outlier flags for supplied expense claims records (claimed_amount, approved_amount). 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-finance-17-130?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","instrument_group","date","claimed_amount","approved_amount"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"claimed_amount":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"approved_amount":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"instrument_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","claimed_amount":10,"approved_amount":1,"instrument_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","claimed_amount":20,"approved_amount":2,"instrument_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","claimed_amount":15,"approved_amount":3,"instrument_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","claimed_amount":30,"approved_amount":4,"instrument_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["claimed_amount","approved_amount"],"type":"string","default":"claimed_amount"},"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_8v7IAqx5VflB4MPaahEn-","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":"Filters expense claim rows by a numeric amount range and flags IQR-based outliers across claimed_amount and approved_amount fields for up to 200 rows.","exampleAgentPrompt":"Can you analyze these 50 expense claim rows and flag any IQR outliers in the claimed_amount field, filtering to only rows where claimed_amount is between 0 and 5000? Here's the data: [row-1, group-a, 2024-03-01, 120, 100], [row-2, group-b, 2024-03-02, 9500, 200]…","exampleUseCases":[{"title":"Audit team outlier detection","prompt":"I have 80 expense claims from last quarter and want to know which ones have claimed_amount values that are statistical outliers using IQR — please filter to amounts between 0 and 2000 and flag anything suspicious."},{"title":"Approved amount anomaly review","prompt":"Can you run an IQR outlier check on the approved_amount field across these 120 expense records, keeping only rows where approved_amount falls between 50 and 10000? I need to catch anything that looks abnormal before month-end close."},{"title":"Department expense range compliance","prompt":"Check these 45 expense claims for group-finance and flag any claimed_amount outliers — filter to the range 100 to 3000 and tell me which rows fall outside the IQR bounds."}],"resultDescription":"Returns two analytical reports for the supplied expense claim rows: (1) a numeric interval filter report listing which rows have the specified field value within the given minimum–maximum range, and (2) an IQR outlier flag report identifying which rows have values statistically outside the interquartile range. Both reports cover up to 200 input rows and operate entirely on 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, claimed_amount, approved_amount, instrument_group) cause validation errors","Date field not matching YYYY-MM-DD pattern results in schema rejection","Minimum greater than maximum produces an empty interval filter result","All null numeric values in the target field may produce undefined IQR results","Payment of 0.01 USDC not included or not accepted causes 402 response"],"whenToPreferThis":"Choose this endpoint when you have a batch of up to 200 expense claim rows and need both a numeric range filter and IQR-based outlier detection in a single low-cost call. It is purpose-built for expense data with claimed_amount and approved_amount fields, making it more directly applicable than generic statistical APIs. Prefer it when you want offline, deterministic analysis without live lookups or external data enrichment.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-30T12:38:02.113Z","isFirstParty":false,"canonicalSlug":"expense-claims-numeric-interval-filter-and-iqr-outlier-flags-5e8b607d"}