{"uid":"cap_Ler3B2dFBSBANuHU2smpZ","slug":"join-coverage-analytics-exact-row-deduplication-date-by-category-pivot-acd5f3f3","name":"Join Coverage Analytics — Exact Row Deduplication + Date by Category Pivot","description":"Join coverage — Exact row deduplication + Date by category pivot: Exact row deduplication + Date by category pivot 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-09-052?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},"field":{"enum":["matched_rows","source_rows"],"type":"string","default":"matched_rows"}}},"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_8jUW4j4ZoQA52R8A2m1FD","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 analytical reports on join coverage data: exact row deduplication and a date-by-category pivot table, for up to 200 supplied rows.","exampleAgentPrompt":"I have up to 200 join coverage records with row IDs, dataset groups, dates, matched_rows, and source_rows — can you deduplicate the exact duplicate rows and also give me a pivot table of matched_rows broken out by date and dataset group?","exampleUseCases":[{"title":"Deduplicating ETL join coverage audit logs","prompt":"I've got a batch of join coverage records from our ETL pipeline — some rows are exact duplicates. Can you deduplicate them and also pivot the matched_rows counts by date and dataset group so I can see coverage trends? Here are the rows with row_id, dataset_group, date, matched_rows, and source_rows."},{"title":"Daily coverage pivot for multi-group data reconciliation","prompt":"Can you take my join coverage data for groups 'sales' and 'returns' across October 2026, deduplicate any exact row duplicates, and give me a pivot of source_rows by date and category?"},{"title":"Pre-report data cleanup for join quality dashboard","prompt":"Before I build my dashboard, I need to clean up these join coverage records — remove any exact duplicate rows and pivot the matched_rows field by date and dataset group. I have about 150 rows with the standard fields."}],"resultDescription":"Returns two analytical reports: (1) a deduplicated version of the input rows after removing exact duplicate records, and (2) a pivot table of the selected metric field (matched_rows or source_rows) broken out by date on one axis and dataset_group on the other, enabling cross-group temporal analysis of join coverage.","failureModes":["Input array is empty or exceeds 200 rows — validation error returned","Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) on any row — schema validation failure","Invalid date format (not YYYY-MM-DD) — pattern validation error","field enum value not one of 'matched_rows' or 'source_rows' — rejected","row_id or dataset_group exceeds 80 character limit — field truncation or rejection","Payment of 0.01 USDC not completed via x402 protocol — 402 Payment Required response"],"whenToPreferThis":"Choose this endpoint when you have up to 200 join coverage records and need both exact row deduplication and a date-by-category pivot in a single call. It is purpose-built for join coverage metrics (matched_rows, source_rows) and is more efficient than running two separate analytics steps. Prefer it over generic data tools when you need a combined clean+pivot report without any live lookup or external data enrichment.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T18:54:29.690Z","isFirstParty":false,"canonicalSlug":"join-coverage-analytics-exact-row-deduplication-date-by-category-pivot-acd5f3f3"}