{"uid":"cap_c5HnyS9jqfwqG2me6uPta","slug":"travel-administration-string-normalisation-iqr-outlier-flags-e9e047fb","name":"Travel Administration — String Normalisation + IQR Outlier Flags","description":"Travel administration — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied travel administration records (trip_count, recorded_cost). 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-operations-14-032?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","team_group","date","trip_count","recorded_cost"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"team_group":{"type":"string","default":"","maxLength":80},"trip_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"recorded_cost":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","team_group":"group-a","trip_count":10,"recorded_cost":1},{"date":"2026-10-02","row_id":"row-2","team_group":"group-b","trip_count":20,"recorded_cost":2},{"date":"2026-10-03","row_id":"row-3","team_group":"group-a","trip_count":15,"recorded_cost":3},{"date":"2026-10-04","row_id":"row-4","team_group":"group-b","trip_count":30,"recorded_cost":4}],"maxItems":200,"minItems":1},"field":{"enum":["trip_count","recorded_cost"],"type":"string","default":"trip_count"}}},"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__8Fo_VB5RJEXSUdOAJ8mN","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 flags IQR-based outliers in travel administration records containing trip count and recorded cost data, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you run string normalisation and IQR outlier detection on these travel records — I have up to 50 rows with row IDs, team groups, dates, trip counts, and recorded costs — and flag any outliers on the trip_count field?","exampleUseCases":[{"title":"Quarterly travel expense anomaly audit","prompt":"I've got 120 rows of travel administration data with team groups, dates, trip counts, and recorded costs — can you normalise the strings and flag any IQR outliers on the recorded_cost field so I know which entries look suspicious?"},{"title":"Team trip count outlier detection","prompt":"Run IQR outlier flagging on our travel records for trip_count — I have about 80 rows covering multiple team groups and dates across the last quarter. Flag anything that looks statistically unusual."},{"title":"Travel data clean-up before reporting","prompt":"Before I generate our monthly travel report, can you normalise the team group strings and check for outliers in trip_count across these 60 travel administration rows so the data is clean?"}],"resultDescription":"Returns two analytical reports per call: one covering string normalisation applied to the supplied travel records and one covering IQR-based outlier flags for the selected numeric field (trip_count or recorded_cost), covering up to 200 input rows. No external data is fetched — analysis is performed locally on the submitted data.","failureModes":["More than 200 rows submitted — request rejected or truncated","Missing required fields (row_id, date, trip_count, recorded_cost) cause validation errors","Invalid date format (not YYYY-MM-DD) causes schema rejection","Null-only numeric field selected for IQR analysis may yield no meaningful outlier flags","Numeric values outside allowed range (±1,000,000,000,000) cause validation failure"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local statistical analysis of travel administration records — specifically string normalisation plus IQR outlier detection — without needing live data lookup, predictive modelling, or external integrations. Best suited for batch auditing of up to 200 rows of trip/cost data at low cost ($0.01 USDC for two reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-29T00:35:48.495Z","isFirstParty":false,"canonicalSlug":"travel-administration-string-normalisation-iqr-outlier-flags-e9e047fb"}