{"uid":"cap_RAQXNv7XbWqt4Z-6Xn4ib","slug":"humidity-observations-string-normalisation-duplicate-key-report-6aafe417","name":"Humidity Observations — String Normalisation + Duplicate Key Report","description":"Humidity observations — String normalisation + Duplicate key report: String normalisation + Duplicate key report for supplied humidity observations records (humidity_percent, observation_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-weather-04-021?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","humidity_percent","observation_count"],"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},"humidity_percent":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"observation_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","station_group":"group-a","humidity_percent":10,"observation_count":1},{"date":"2026-10-02","row_id":"row-2","station_group":"group-b","humidity_percent":20,"observation_count":2},{"date":"2026-10-03","row_id":"row-3","station_group":"group-a","humidity_percent":15,"observation_count":3},{"date":"2026-10-04","row_id":"row-4","station_group":"group-b","humidity_percent":30,"observation_count":4}],"maxItems":200,"minItems":1}}},"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_A6CqD74r7RZQ0giAFGMCf","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 string normalisation and duplicate key detection on uploaded humidity observation records (humidity_percent, observation_count), returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you run a string normalisation check and duplicate key report on my humidity observation records? I have about 50 rows with fields like station_group, date, humidity_percent, and observation_count — I want to catch any duplicated row IDs and clean up inconsistent string values.","exampleUseCases":[{"title":"Weather station data quality audit","prompt":"I've got a batch of humidity readings from multiple station groups and I need to check for duplicate row IDs and normalise the station group name strings before I load them into our database — can you run the string normalisation and duplicate key reports on these 80 rows?"},{"title":"Pre-pipeline humidity data cleansing","prompt":"Before I merge these humidity observation records into our analytics pipeline, can you scan them for duplicate keys and normalise any inconsistent text fields? There are about 120 rows with humidity_percent and observation_count values across several station groups."},{"title":"Data validation before reporting","prompt":"I'm preparing a humidity dataset for a monthly environmental report and need to make sure there are no duplicate entries and the string fields are consistently formatted — can you run both the duplicate key and string normalisation reports on my 35 observation rows?"}],"resultDescription":"Two analytical reports: (1) a string normalisation report identifying and standardising inconsistent string values across text fields (row_id, station_group, date), and (2) a duplicate key report flagging rows with repeated or conflicting key combinations, covering up to 200 input rows.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Missing required fields (row_id, date) cause row-level validation errors","Malformed date strings not matching YYYY-MM-DD pattern are rejected","Empty rows array returns an error","Numeric values outside allowed range may cause processing errors"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation and duplicate key detection specifically on humidity observation records (humidity_percent, observation_count) in a single low-cost call. It is ideal for pre-ingestion data quality checks on weather station datasets of up to 200 rows where no live data lookup, forecasting, or external enrichment is needed.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T12:31:45.480Z","isFirstParty":false,"canonicalSlug":"humidity-observations-string-normalisation-duplicate-key-report-6aafe417"}