{"uid":"cap_vD9IdKcwTDc32CutU7Oso","slug":"road-segment-observations-record-quality-audit-adjacent-row-changes-389cf2b7","name":"Road Segment Observations — Record Quality Audit + Adjacent Row Changes","description":"Road segment observations — Record quality audit + Adjacent row changes: Record quality audit + Adjacent row changes for supplied road segment observations records (length_m, travel_seconds). 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-weather-15-011?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","length_m","travel_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"length_m":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"station_group":{"type":"string","default":"","maxLength":80},"travel_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","length_m":10,"station_group":"group-a","travel_seconds":1},{"date":"2026-10-02","row_id":"row-2","length_m":20,"station_group":"group-b","travel_seconds":2},{"date":"2026-10-03","row_id":"row-3","length_m":15,"station_group":"group-a","travel_seconds":3},{"date":"2026-10-04","row_id":"row-4","length_m":30,"station_group":"group-b","travel_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["length_m","travel_seconds"],"type":"string","default":"length_m"}}},"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_FYo_TbdDGHwt4hVo5yv_B","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":"Audits record quality and detects adjacent row changes in road segment observation data (length_m, travel_seconds) for up to 200 rows, returning two analytical reports.","exampleAgentPrompt":"Can you run a record quality audit and adjacent row changes check on these road segment observations? I have up to 50 rows with fields row_id, station_group, date, length_m, and travel_seconds — check the length_m field and flag any quality issues or abrupt changes between consecutive rows.","exampleUseCases":[{"title":"Road sensor data quality check","prompt":"I've got a batch of road segment readings from our sensors — row IDs, station groups, dates, length in meters, and travel seconds. Can you audit these records for quality issues and also flag any sudden changes between adjacent rows, focusing on the length_m field? There are about 80 rows."},{"title":"Travel time consistency validation","prompt":"Before I load this dataset into our traffic model, can you check the quality of these road segment observations and detect any big jumps between consecutive rows in the travel_seconds field? I have around 120 rows with station groups and dates."},{"title":"Automated pipeline data integrity check","prompt":"Run a record quality audit and adjacent row change detection on this road segment data — about 30 rows covering station groups and daily measurements. Flag any records that look malformed and show me where travel_seconds changes dramatically between neighboring rows."}],"resultDescription":"Returns two independent analytical reports: (1) a record quality audit identifying malformed, missing, or out-of-range values across the supplied rows, and (2) an adjacent row changes report showing where consecutive rows differ significantly in the selected numeric field (length_m or travel_seconds). Both reports are generated locally without external lookups.","failureModes":["More than 200 rows submitted — request rejected with validation error","Missing required fields (row_id, date, length_m, travel_seconds) — row-level validation error","Invalid date format (not YYYY-MM-DD) — schema validation failure","Invalid field enum value (not length_m or travel_seconds) — rejected","Empty rows array — rejected with minItems violation","Payment not provided or insufficient — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you need two simultaneous analytical reports on road segment observation data: a record quality audit and adjacent row change detection, without needing live traffic lookups or external data enrichment. It is best suited for batch validation of structured road datasets up to 200 rows where you want to catch bad records and sudden value shifts between consecutive rows in a single low-cost call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T12:41:37.980Z","isFirstParty":false,"canonicalSlug":"road-segment-observations-record-quality-audit-adjacent-row-changes-389cf2b7"}