{"uid":"cap_lJP3bkIuwGaOpVf5DgN6B","slug":"forecast-verification-inputs-string-normalisation-numeric-row-ordering-f399f113","name":"Forecast Verification Inputs — String Normalisation + Numeric Row Ordering","description":"Forecast verification inputs — String normalisation + Numeric row ordering: String normalisation + Numeric row ordering for supplied forecast verification inputs records (forecast_value, observed_value). 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-24-025?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","forecast_value","observed_value"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"station_group":{"type":"string","default":"","maxLength":80},"forecast_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"observed_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","station_group":"group-a","forecast_value":10,"observed_value":1},{"date":"2026-10-02","row_id":"row-2","station_group":"group-b","forecast_value":20,"observed_value":2},{"date":"2026-10-03","row_id":"row-3","station_group":"group-a","forecast_value":15,"observed_value":3},{"date":"2026-10-04","row_id":"row-4","station_group":"group-b","forecast_value":30,"observed_value":4}],"maxItems":200,"minItems":1},"field":{"enum":["forecast_value","observed_value"],"type":"string","default":"forecast_value"}}},"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_5pKt3C3Yw3Tfq0tUIQccW","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 orders rows numerically for up to 200 forecast verification records (forecast_value, observed_value), returning 2 analytical reports per call.","exampleAgentPrompt":"Can you normalise the string fields and sort these forecast verification rows numerically by forecast_value? I have 4 records: row-1 for group-a on 2026-10-01 with forecast 10 and observed 1, row-2 for group-b on 2026-10-02 with forecast 20 and observed 2, row-3 for group-a on 2026-10-03 with forecast 15 and observed 3, and row-4 for group-b on 2026-10-04 with forecast 30 and observed 4 — give me both analytical reports.","exampleUseCases":[{"title":"Weather station forecast data cleanup","prompt":"I have a batch of 50 weather station forecast verification records with messy station group strings and unsorted forecast values — can you normalise the strings and order the rows numerically by forecast_value so I can compare them cleanly?"},{"title":"Observed vs forecast ranking for model audit","prompt":"Sort these 120 forecast verification rows by observed_value numerically and normalise the station_group strings — I need both reports so I can audit where our model drifted the most."},{"title":"Multi-group forecast dataset preparation","prompt":"I've got forecast and observed readings for group-a and group-b across 80 dates and need to clean up the string fields and get the rows in numeric order by forecast_value before I hand this off to my verification pipeline — can you run the two reports?"}],"resultDescription":"Two analytical reports per call: one applying string normalisation to text fields (e.g. station_group) and one ordering up to 200 rows by the selected numeric field (forecast_value or observed_value), using local analytical engines with no external lookups.","failureModes":["More than 200 rows submitted — request rejected at maxItems constraint","Invalid date format not matching YYYY-MM-DD pattern — validation error","Missing required fields (row_id, station_group, date, forecast_value, observed_value) — schema validation failure","Numeric values outside ±1,000,000,000,000 range — rejected","Empty rows array — minItems:1 violation","Invalid field enum value — must be 'forecast_value' or 'observed_value'"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local string normalisation and numeric row ordering specifically for forecast verification data (forecast_value / observed_value pairs) without requiring live weather lookups, predictions, or external data enrichment. Best suited for pre-processing pipelines where you want two bundled reports cheaply ($0.01 USDC for up to 200 rows) rather than building custom sort/normalise logic.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-09T06:42:19.884Z","isFirstParty":false,"canonicalSlug":"forecast-verification-inputs-string-normalisation-numeric-row-ordering-f399f113"}