{"uid":"cap__LhDLLYVlCQQt3NlDSyW0","slug":"duplicate-observations-string-normalisation-duplicate-key-report-32da5281","name":"Duplicate Observations — String Normalisation + Duplicate Key Report","description":"Duplicate observations — String normalisation + Duplicate key report: String normalisation + Duplicate key report for supplied duplicate observations records (duplicate_rows, total_rows). 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-analytics-04-021?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","duplicate_rows","total_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"total_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"duplicate_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","total_rows":1,"dataset_group":"group-a","duplicate_rows":10},{"date":"2026-10-02","row_id":"row-2","total_rows":2,"dataset_group":"group-b","duplicate_rows":20},{"date":"2026-10-03","row_id":"row-3","total_rows":3,"dataset_group":"group-a","duplicate_rows":15},{"date":"2026-10-04","row_id":"row-4","total_rows":4,"dataset_group":"group-b","duplicate_rows":30}],"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_BMxRJKNBFFk4O5l6mND7I","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 two local analytical reports on supplied duplicate-observation records: string normalisation and duplicate key detection, for up to 200 rows.","exampleAgentPrompt":"Can you run string normalisation and a duplicate key report on my observation dataset? I have 4 rows: row-1 for group-a on 2026-10-01 with 10 duplicates out of 1 total, row-2 for group-b on 2026-10-02 with 20 duplicates out of 2 total, row-3 for group-a on 2026-10-03 with 15 duplicates out of 3 total, and row-4 for group-b on 2026-10-04 with 30 duplicates out of 4 total.","exampleUseCases":[{"title":"Audit ETL pipeline duplicate keys","prompt":"I ran an ETL job and suspect there are duplicate keys across my dataset groups — can you run a duplicate key report on these 8 rows? Each row has a row_id, a group name like 'sales' or 'returns', a date between 2026-09-01 and 2026-09-08, and duplicate_rows/total_rows counts I'll supply."},{"title":"Normalise strings in observation batch","prompt":"My team ingested survey observation records with inconsistent string formatting across dataset groups. Can you run string normalisation on these 50 rows so I can see what's been standardised, along with a duplicate key check? I'll provide the row_ids, groups, dates, and duplicate counts."},{"title":"Data quality check before warehouse merge","prompt":"Before I merge two datasets into our warehouse, I need to check for duplicate keys and normalise string fields. Can you analyse these 120 observation rows — each has a row_id, a dataset_group, a date in YYYY-MM-DD format, and duplicate_rows versus total_rows — and give me both reports?"}],"resultDescription":"Returns two independent analytical reports for the supplied rows: (1) a string normalisation report showing standardised string values across the dataset groups, and (2) a duplicate key report identifying key collisions or duplicate entries within the supplied observation records. Both reports are generated locally from the supplied data with no external lookups.","failureModes":["Exceeding 200 rows causes request rejection","Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) result in validation errors","Dates not matching YYYY-MM-DD pattern fail schema validation","Numeric values outside allowed range cause rejection","Empty rows array fails minItems constraint"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation and duplicate key detection on a single batch of up to 200 observation rows at low cost ($0.01 USDC for both reports). It is ideal for offline/local analytical audits where no live lookups or external data enrichment are needed, and when you want two complementary data quality reports in a single call. If you only need deduplication or only need string normalisation as separate operations, consider sibling endpoints.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:19:34.748Z","isFirstParty":false,"canonicalSlug":"duplicate-observations-string-normalisation-duplicate-key-report-32da5281"}