{"uid":"cap_KvctnEy9vnijd7UAUK_SF","slug":"null-coverage-analytics-grouped-numeric-summary-date-by-category-pivot-eba2b31b","name":"Null Coverage Analytics — Grouped Numeric Summary & Date-by-Category Pivot","description":"Null coverage — Grouped numeric summary + Date by category pivot: Grouped numeric summary + Date by category pivot for supplied null coverage records (missing_values, total_values). 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-03-097?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","missing_values","total_values"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"total_values":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"missing_values":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","total_values":1,"dataset_group":"group-a","missing_values":10},{"date":"2026-10-02","row_id":"row-2","total_values":2,"dataset_group":"group-b","missing_values":20},{"date":"2026-10-03","row_id":"row-3","total_values":3,"dataset_group":"group-a","missing_values":15},{"date":"2026-10-04","row_id":"row-4","total_values":4,"dataset_group":"group-b","missing_values":30}],"maxItems":200,"minItems":1},"field":{"enum":["missing_values","total_values"],"type":"string","default":"missing_values"}}},"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_28MpeKeGyYWx_NfCsjbKM","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 local analytical reports (grouped numeric summary and date-by-category pivot) for null/missing-value coverage data supplied by the caller.","exampleAgentPrompt":"Analyze my null coverage data — I have up to 200 rows each with a row_id, dataset_group, date, missing_values, and total_values — and give me both a grouped numeric summary and a date-by-category pivot for the missing_values field.","exampleUseCases":[{"title":"Data Quality Audit by Team","prompt":"I have 150 rows of null coverage records from our ETL pipeline, each tagged with a dataset_group, date, missing_values, and total_values. Can you generate a grouped numeric summary and a date-by-category pivot for missing_values so I can see which teams have the worst data completeness?"},{"title":"Monthly Completeness Report for Stakeholders","prompt":"Here are my 80 data quality records for Q3 — each has a dataset_group, date, total_values, and missing_values. Run the grouped numeric summary and date-by-category pivot on total_values so I can include the breakdown in our monthly stakeholder report."},{"title":"Debugging Spike in Missing Values","prompt":"We saw a spike in missing data last week. I have 200 rows with row_id, dataset_group, date, missing_values, and total_values. Can you give me a grouped numeric summary and a date-by-category pivot on missing_values so I can pinpoint which groups and dates are responsible?"}],"resultDescription":"Returns two reports: (1) a grouped numeric summary of the selected field (missing_values or total_values) broken down by dataset_group, and (2) a pivot table of that field by date and category — both computed locally from the supplied rows, with no external lookups or predictions.","failureModes":["Missing required row fields (row_id, dataset_group, date, missing_values, total_values) causes validation error","More than 200 rows submitted exceeds the row limit and will be rejected","Invalid date format (not YYYY-MM-DD) causes schema validation failure","Selecting a field value outside the enum [missing_values, total_values] returns an error","Submitting an empty rows array (minItems:1 violated) causes rejection","Null values for numeric fields may affect aggregation results if not handled by caller"],"whenToPreferThis":"Choose this endpoint when you have pre-collected null coverage records (with missing_values and total_values per row) and need two analytical views — a grouped numeric summary and a date-by-category pivot — computed entirely from your supplied data without any live lookups or external data enrichment. Ideal for data quality audits, ETL pipeline monitoring, and completeness reporting where the data is already on hand and you need structured analytical output at very low cost ($0.01 USDC for both reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T06:49:49.335Z","isFirstParty":false,"canonicalSlug":"null-coverage-analytics-grouped-numeric-summary-date-by-category-pivot-eba2b31b"}