{"uid":"cap_gq7uzKK4Hf4i_2ldF-sSU","slug":"join-coverage-analytics-string-normalisation-numeric-row-ordering-5b08d47b","name":"Join Coverage Analytics — String Normalisation + Numeric Row Ordering","description":"Join coverage — String normalisation + Numeric row ordering: String normalisation + Numeric row ordering 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-09-025?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_m44xoSh1cqxA6DMhztG6I","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":"Normalises string fields and orders rows numerically for up to 200 join coverage records (matched_rows, source_rows), producing 2 independent analytical reports per call.","exampleAgentPrompt":"Can you run string normalisation and numeric row ordering on my join coverage data? I have 4 rows — row-1 through row-4 dated 2026-10-01 to 2026-10-04 with matched_rows values of 10, 20, 15, and 30 across groups group-a and group-b, and I want the report on the matched_rows field.","exampleUseCases":[{"title":"ETL pipeline join quality audit","prompt":"I've got a batch of 50 join coverage records from our nightly ETL run with row IDs, dates, dataset groups, and both source_rows and matched_rows counts — can you normalise the string fields and order them numerically by matched_rows so I can spot which joins are underperforming?"},{"title":"Dataset group comparison by source rows","prompt":"Run the join coverage analytics on my 12 records across dataset groups alpha and beta, covering dates from 2026-09-01 to 2026-09-12, and order everything by source_rows so I can see which groups contributed the most data."},{"title":"Daily join coverage reporting for data team","prompt":"Here are today's 30 join coverage rows with their matched_rows and source_rows — can you normalise the dataset_group strings and give me two analytical reports ordered numerically so our data team can review coverage gaps?"}],"resultDescription":"Returns two independent analytical reports for the supplied join coverage rows: one with string fields normalised (e.g. dataset_group, row_id trimmed and standardised) and one with rows ordered numerically by the selected field (matched_rows or source_rows). No external lookups or predictions are performed — all processing is local and deterministic.","failureModes":["Input array is empty (minItems: 1 not met) — returns validation error","More than 200 rows submitted — returns rejection due to maxItems limit","Invalid date format (not YYYY-MM-DD) — returns schema validation error","row_id missing or empty string — fails required field validation","Numeric fields outside allowed range (±1,000,000,000,000) — returns range error","Payment not included or insufficient — 402 Payment Required response"],"whenToPreferThis":"Choose this endpoint when you have structured join coverage data (matched_rows, source_rows) and need both string normalisation and numeric ordering in a single cheap call without any external data lookups or ML predictions. Ideal for data engineers auditing ETL pipeline join quality on batches up to 200 rows at $0.01 per call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T19:04:56.817Z","isFirstParty":false,"canonicalSlug":"join-coverage-analytics-string-normalisation-numeric-row-ordering-5b08d47b"}