{"uid":"cap_q_ruGs6vUO680pgT2VgK5","slug":"join-coverage-analytics-record-quality-audit-date-by-category-pivot-b5d957b7","name":"Join Coverage Analytics — Record Quality Audit + Date by Category Pivot","description":"Join coverage — Record quality audit + Date by category pivot: Record quality audit + 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-09-017?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_RUtwsRDG8zbet8_BAuepZ","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 join coverage data (matched_rows, source_rows): a record quality audit and a date-by-category pivot, for up to 200 rows.","exampleAgentPrompt":"Can you run a record quality audit and date-by-category pivot on my join coverage data? Here are my rows — each has a row_id, dataset_group, date, matched_rows, and source_rows — and I want to analyze the matched_rows field across up to 200 records.","exampleUseCases":[{"title":"ETL pipeline join quality check","prompt":"I have output from my ETL join process — about 150 rows with row IDs, dataset groups, dates, and matched_rows vs source_rows counts. Can you run a record quality audit and a date-by-category pivot on the matched_rows field so I can see where coverage is dropping?"},{"title":"Daily dataset match rate review","prompt":"I want to audit today's join results across my two dataset groups — group-a and group-b — over the past month. I've got ~80 rows with dates and matched/source row counts. Please give me a quality audit and pivot the source_rows by date and group."},{"title":"Data reconciliation anomaly detection","prompt":"Before I sign off on this data reconciliation, can you run a quality audit and a date-by-category pivot on my join coverage records? I have 60 rows from three groups with matched_rows and source_rows so I can spot any mismatched dates or groups with bad coverage."}],"resultDescription":"Two analytical reports: (1) a record quality audit highlighting issues such as nulls, out-of-range values, and anomalies in the selected numeric field (matched_rows or source_rows); and (2) a date-by-category pivot table summarizing the chosen metric across dataset_group and date dimensions — all computed locally on the supplied data with no external calls.","failureModes":["Input array is empty or exceeds 200 rows — validation error returned","Required fields (row_id, dataset_group, date, matched_rows, source_rows) missing from any row — schema validation failure","Date field does not match YYYY-MM-DD pattern — rejected with format error","Numeric values outside allowed range (±1,000,000,000,000) — validation error","Invalid field enum value (not 'matched_rows' or 'source_rows') — rejected"],"whenToPreferThis":"Choose this endpoint when you have join coverage records with matched_rows and source_rows and need two simultaneous analytical views — a quality audit plus a date-by-category pivot — in a single cheap call ($0.01 USDC). Ideal for data engineers validating ETL join outputs, reconciling dataset coverage, or spot-checking match rates across groups and dates without needing live lookups or external data enrichment.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:42.938Z","isFirstParty":false,"canonicalSlug":"join-coverage-analytics-record-quality-audit-date-by-category-pivot-b5d957b7"}