{"uid":"cap_h1XzY2Ej7TR-ZfDpkApmj","slug":"road-segment-observations-record-quality-audit-string-normalisation-f9cf1796","name":"Road Segment Observations — Record Quality Audit + String Normalisation + Adjacent Row Changes","description":"Road segment observations — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes for supplied road segment observations records (length_m, travel_seconds). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-weather-15-200?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_h3bCTs_zWAEz53yJK0nub","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":"Runs three analytical reports (record quality audit, string normalisation, and adjacent row changes) on supplied road segment observation data (length_m, travel_seconds) for up to 200 rows.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and adjacent row changes analysis on this batch of road segment observations — I have up to 150 rows with fields row_id, station_group, date, length_m, and travel_seconds, and I want to check the length_m field for sequential changes?","exampleUseCases":[{"title":"Pre-analysis data quality check","prompt":"I've got a dataset of 120 road segment records with station groups, dates, length in metres, and travel seconds — can you audit the record quality, normalise the station group strings, and flag any adjacent row changes in the length_m field before I load this into my pipeline?"},{"title":"Detecting speed anomalies in consecutive readings","prompt":"I need to find where travel_seconds changed significantly between consecutive rows in my road segment observation data — can you run the adjacent row changes report alongside a quality audit on these 80 rows?"},{"title":"Cleaning station group labels before reporting","prompt":"My road segment CSV has messy station_group values — can you normalise those strings and also run a quality audit on the 200 rows so I can see what's dirty before I send the report to the team?"}],"resultDescription":"Three independent analytical reports: (1) a record quality audit flagging completeness, format issues, and anomalies in each row; (2) a string normalisation report cleaning and standardising the station_group field; and (3) an adjacent row changes report showing value differences between consecutive rows for the selected field (length_m or travel_seconds). All computed locally with no external lookups.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Missing required fields (row_id, date, length_m, travel_seconds) causes row-level errors","Date values not matching YYYY-MM-DD pattern may fail validation","Null numeric values in length_m or travel_seconds may affect adjacent row change calculations","Invalid field enum value (not 'length_m' or 'travel_seconds') causes parameter rejection","Empty rows array returns an error"],"whenToPreferThis":"Choose this endpoint when you have road segment observation data (with length_m and travel_seconds fields) and need three bundled analytical reports — quality audit, string normalisation, and adjacent row change detection — in a single low-cost call. It is ideal for pre-processing pipelines where data cleanliness and sequential anomaly detection are needed together before downstream analysis, and where no live external lookups or predictions are required.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T12:37:31.043Z","isFirstParty":false,"canonicalSlug":"road-segment-observations-record-quality-audit-string-normalisation-f9cf1796"}