{"uid":"cap_YbGIxP3faUZ25WfEdAnpQ","slug":"join-coverage-string-normalisation-exact-row-deduplication-4c5bc035","name":"Join Coverage — String Normalisation + Exact Row Deduplication","description":"Join coverage — String normalisation + Exact row deduplication: String normalisation + Exact row deduplication for supplied join coverage records (matched_rows, source_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-09-020?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","matched_rows","source_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"matched_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_rows":1,"matched_rows":10,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","source_rows":2,"matched_rows":20,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","source_rows":3,"matched_rows":15,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","source_rows":4,"matched_rows":30,"dataset_group":"group-b"}],"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_Zro6LUn_eLwwtUDqQV0CV","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 strings and deduplicates exact rows in supplied join coverage records (matched_rows, source_rows), returning two independent analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you clean up my join coverage data? I have 50 rows with dataset_group, date, matched_rows, and source_rows fields — please normalise the strings and remove any exact duplicate rows, then give me both reports.","exampleUseCases":[{"title":"ETL pipeline join coverage cleanup","prompt":"I'm building a data pipeline and have 120 join coverage records across two dataset groups — 'orders' and 'returns' — with dates from 2025-01-01 to 2025-04-30. Can you normalise the strings and deduplicate exact rows, then give me both the normalisation and deduplication reports?"},{"title":"Data audit before warehouse load","prompt":"Before I load this join coverage batch into the warehouse, can you run string normalisation and exact row deduplication on these 80 records? The rows have matched_rows, source_rows, dataset_group, and date fields and I want to catch any dirty or duplicate entries."},{"title":"Reconciliation report for matched dataset groups","prompt":"I've got 35 join coverage rows for dataset groups 'invoices' and 'payments' across March 2025 — can you normalise all the string fields and identify any exactly duplicated rows, and give me both analytical reports back?"}],"resultDescription":"Two independent analytical reports: (1) a string normalisation report identifying and standardising inconsistent string values across row fields, and (2) an exact row deduplication report identifying and removing fully duplicated rows — both covering the up to 200 supplied join coverage records.","failureModes":["Rows array exceeds 200 items — request rejected","Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) in any row — validation error","date field not matching YYYY-MM-DD pattern — schema validation failure","matched_rows or source_rows outside allowed numeric range — rejected","Empty rows array (minItems: 1 not met) — error returned","Payment of 0.01 USDC not received — request blocked"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation and exact row deduplication applied together to join coverage records (matched_rows, source_rows) in a single low-cost call, without requiring live lookups, predictions, or external data sources. It is ideal for batch ETL cleanup tasks on datasets of up to 200 rows where two simultaneous analytical reports are needed at minimal cost.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T18:54:11.356Z","isFirstParty":false,"canonicalSlug":"join-coverage-string-normalisation-exact-row-deduplication-4c5bc035"}