{"uid":"cap_ZB-wgJ6EYE8jbSeTROIAY","slug":"transaction-fee-observations-exact-row-deduplication-iqr-outlier-flags-010284b6","name":"Transaction Fee Observations — Exact Row Deduplication + IQR Outlier Flags","description":"Transaction fee observations — Exact row deduplication + IQR outlier flags: Exact row deduplication + IQR outlier flags for supplied transaction fee observations records (fee_units, transaction_count). 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-03-049?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","instrument_group","date","fee_units","transaction_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"fee_units":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"instrument_group":{"type":"string","default":"","maxLength":80},"transaction_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","fee_units":10,"instrument_group":"group-a","transaction_count":1},{"date":"2026-10-02","row_id":"row-2","fee_units":20,"instrument_group":"group-b","transaction_count":2},{"date":"2026-10-03","row_id":"row-3","fee_units":15,"instrument_group":"group-a","transaction_count":3},{"date":"2026-10-04","row_id":"row-4","fee_units":30,"instrument_group":"group-b","transaction_count":4}],"maxItems":200,"minItems":1},"field":{"enum":["fee_units","transaction_count"],"type":"string","default":"fee_units"}}},"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_foT72dKVWqTf2vgn2TojI","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":"Deduplicates transaction fee observation records and flags statistical outliers using IQR analysis across fee_units or transaction_count fields.","exampleAgentPrompt":"Can you deduplicate my transaction fee observations and flag any IQR outliers in the fee_units field? I have about 50 rows with columns row_id, instrument_group, date, fee_units, and transaction_count.","exampleUseCases":[{"title":"Cleaning duplicate fee records","prompt":"I have a batch of 120 transaction fee records across instrument groups like equities and FX — can you remove any exact duplicate rows and tell me which ones were dupes, checking the fee_units field?"},{"title":"Flagging anomalous transaction counts","prompt":"Run an IQR outlier check on my transaction_count column across these 80 fee observation rows — I want to know which records have unusually high or low counts compared to the rest of the dataset."},{"title":"Pre-reporting data quality check","prompt":"Before I finalize my fee report, can you deduplicate these 200 rows of transaction fee data and flag any outliers in fee_units so I know which records to investigate before submitting?"}],"resultDescription":"Returns two analytical reports: (1) an exact row deduplication report identifying which rows are duplicates and which are unique, and (2) an IQR outlier flagging report identifying which rows have fee_units or transaction_count values that fall outside the interquartile range bounds, with outlier thresholds noted.","failureModes":["Input array exceeds 200 rows — request rejected","Missing required fields (row_id, instrument_group, date, fee_units, transaction_count) — validation error","Invalid date format (must match YYYY-MM-DD) — schema validation failure","All rows are identical — deduplication report returns single unique row with all others flagged as duplicates","Too few rows to compute meaningful IQR — outlier flags may be unreliable","Numeric values outside allowed range (-1e12 to 1e12) — rejected at schema validation"],"whenToPreferThis":"Choose this endpoint when you have tabular transaction fee data (up to 200 rows) and need both exact duplicate removal and statistical outlier detection (IQR-based) in a single call, without requiring any live data lookup, external enrichment, or prediction. Ideal for pre-processing fee datasets before reporting or analysis pipelines.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T06:47:24.953Z","isFirstParty":false,"canonicalSlug":"transaction-fee-observations-exact-row-deduplication-iqr-outlier-flags-010284b6"}