{"uid":"cap_Q_H0cH7aadblgInReVTjC","slug":"null-coverage-analytics-string-normalisation-numeric-distribution-b914f711","name":"Null Coverage Analytics: String Normalisation + Numeric Distribution","description":"Null coverage — String normalisation + Numeric distribution: String normalisation + Numeric distribution 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-03-022?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_oOTN14urbToqgeKiWjxO1","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":"Analyzes null/missing value coverage in supplied data rows, producing string normalization and numeric distribution reports for the missing_values or total_values field.","exampleAgentPrompt":"Can you analyze null coverage for my dataset — I have 50 rows each with a row_id, dataset_group, date, missing_values, and total_values — and give me both a string normalization report and a numeric distribution report on the missing_values field?","exampleUseCases":[{"title":"Data quality audit before pipeline run","prompt":"Before I run my ETL pipeline, can you check null coverage on these 120 records — they have row_id, dataset_group, date, missing_values, and total_values columns — and give me both the string normalization and numeric distribution reports on the missing_values field?"},{"title":"Comparing null rates across dataset groups","prompt":"I want to understand how missing values are distributed across my dataset groups. Here are 80 rows with row_id, dataset_group, date, missing_values, and total_values — can you run the string normalization and numeric distribution analysis on the total_values field?"},{"title":"Monthly null coverage summary for reporting","prompt":"I need a monthly null coverage summary for my team report. I have 60 rows of data with dates in October 2026, each with missing_values and total_values counts. Can you produce the string normalization and numeric distribution reports on the missing_values field?"}],"resultDescription":"Returns two analytical reports for the supplied rows (up to 200): a string normalization report (covering dataset_group and related string fields) and a numeric distribution report (covering the selected numeric field — missing_values or total_values). Reports are computed locally with no external lookups or predictions.","failureModes":["Fewer than 1 row supplied — minimum 1 row required","More than 200 rows supplied — request will be rejected or truncated","Invalid date format (must be YYYY-MM-DD)","Missing required fields (row_id, dataset_group, date, missing_values, total_values)","Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000)","Invalid field enum value — must be 'missing_values' or 'total_values'"],"whenToPreferThis":"Choose this endpoint when you have tabular null coverage records (rows with missing_values and total_values counts) and need two fast, locally-computed analytical reports — string normalization and numeric distribution — in a single call for up to 200 rows. It is ideal for lightweight data quality checks without external lookups or ML predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:17.445Z","isFirstParty":false,"canonicalSlug":"null-coverage-analytics-string-normalisation-numeric-distribution-b914f711"}