{"uid":"cap_vnpWngv4Rjfdd3ZJZi1gN","slug":"join-coverage-analytics-category-frequencies-date-by-category-pivot-2b25fe7c","name":"Join Coverage Analytics: Category Frequencies + Date-by-Category Pivot","description":"Join coverage — Category frequencies + Date by category pivot: Category frequencies + 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-09-110?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_4drSmShVl_CntIzRkeSM1","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":"Generates two analytical reports for join coverage data — category frequency distribution and a date-by-category pivot — for up to 200 supplied rows of matched_rows/source_rows metrics.","exampleAgentPrompt":"Can you run a join coverage analysis on my data — I have 4 rows with dataset groups group-a and group-b, dates from 2026-10-01 to 2026-10-04, and matched_rows values of 10, 20, 15, 30 with source_rows of 1, 2, 3, 4 — give me the category frequencies and a date-by-category pivot for the matched_rows field?","exampleUseCases":[{"title":"ETL pipeline join audit by group","prompt":"I have daily join coverage records for two dataset groups — 'customers' and 'orders' — with matched_rows and source_rows for each date in October 2026. Can you generate the category frequency breakdown and a date-by-category pivot table for matched_rows so I can see which group has better join coverage?"},{"title":"Data quality monitoring across sources","prompt":"I'm auditing our data warehouse joins and have 50 rows of coverage records with dataset_group, date, source_rows, and matched_rows. Run the category frequencies and date-by-category pivot for source_rows so I can spot which groups are underperforming."},{"title":"Ad-hoc join rate comparison across datasets","prompt":"Here are 8 join coverage records across dataset groups 'inventory', 'sales', and 'returns' from last week — can you pivot the matched_rows by date and category and also give me a frequency count per category so I can compare join rates?"}],"resultDescription":"Returns two analytical reports: (1) a category frequency table showing how often each dataset_group appears and its aggregate statistics for the selected field (matched_rows or source_rows), and (2) a date-by-category pivot table cross-tabulating dates against dataset groups for the chosen numeric metric — all computed locally from the supplied rows with no external lookups.","failureModes":["Fewer than 1 row or more than 200 rows supplied — rejected by minItems/maxItems validation","Invalid date format (not YYYY-MM-DD) causes schema validation failure","Numeric values outside ±1,000,000,000,000 range rejected","Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) cause validation errors","Invalid field enum value (not 'matched_rows' or 'source_rows') rejected","Payment failure or insufficient USDC balance prevents call execution"],"whenToPreferThis":"Choose this endpoint when you need quick dual-report analytics (frequency + pivot) on join coverage data without setting up a local analytics environment. Ideal for data engineers auditing ETL pipeline join rates across multiple dataset groups and dates, especially when you want both a categorical frequency breakdown and a time-series pivot in a single API call for up to 200 rows at very low cost.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:43.789Z","isFirstParty":false,"canonicalSlug":"join-coverage-analytics-category-frequencies-date-by-category-pivot-2b25fe7c"}