{"uid":"cap_b2hEJXgfYpS8fuUAx7BFf","slug":"join-coverage-daily-sum-series-explicit-missing-value-fill-60a77e29","name":"Join Coverage — Daily Sum Series + Explicit Missing-Value Fill","description":"Join coverage — Daily sum series + Explicit missing-value fill: Daily sum series + Explicit missing-value fill 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-09-145?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"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_hR3fwsLn9D6L4Wvjc6gkr","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":"Computes a daily sum time series for join coverage metrics (matched_rows, source_rows) and fills missing values using zero, mean, or median imputation, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you compute a daily sum series for the matched_rows field in my join coverage data using median fill for missing values? I have records for group-a and group-b across October 2026 — up to 30 rows.","exampleUseCases":[{"title":"ETL pipeline join coverage audit","prompt":"I have daily join coverage records for two dataset groups across October 2026, about 60 rows. Can you compute the daily sums of matched_rows for each day and fill any missing dates using median imputation so I can see where my join rates dropped?"},{"title":"Source row gap detection for data warehouse","prompt":"I need a daily sum series for source_rows in my warehouse join logs — I've got about 150 rows across multiple dataset groups. Please fill any missing-value gaps with zero so I can spot days where no source data arrived."},{"title":"Data quality report on join match trends","prompt":"Can you summarize my join coverage dataset by aggregating matched_rows into a daily sum series, using mean fill for any gaps? I have records tagged under group-a and group-b covering about 90 days and want two analytical reports out of it."}],"resultDescription":"Two analytical reports covering: (1) a daily sum time series for the selected field (matched_rows or source_rows) aggregated across the supplied rows, and (2) an explicit missing-value fill report showing imputed values using the chosen method (zero, mean, or median). Results are based solely on the supplied data with no external lookups.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, date, dataset_group) on any row — validation error","Invalid date format (not YYYY-MM-DD) — schema validation failure","Invalid field or method enum value — schema validation failure","Empty rows array — minItems constraint violation","Numeric values outside allowed range (±1,000,000,000,000) — schema rejection"],"whenToPreferThis":"Choose this endpoint when you need to aggregate daily sum totals and impute missing values for join coverage metrics (matched_rows, source_rows) from supplied tabular data, without requiring external data sources or live lookups. It is ideal for data engineering and ETL audit workflows where you have up to 200 pre-collected join coverage records grouped by date and dataset group, and need two structured analytical reports from a single low-cost call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:41.051Z","isFirstParty":false,"canonicalSlug":"join-coverage-daily-sum-series-explicit-missing-value-fill-60a77e29"}