{"uid":"cap_Ad8_nj1LHPvoLhIFG8s5a","slug":"join-coverage-analytics-string-normalisation-date-by-category-pivot-f121bb40","name":"Join Coverage Analytics: String Normalisation + Date-by-Category Pivot","description":"Join coverage — String normalisation + Date by category pivot: String normalisation + 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-09-035?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_OtfLp29yRWb_ivqrgosP3","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 join coverage records (matched_rows, source_rows) by running string normalisation and a date-by-category pivot report on up to 200 supplied rows.","exampleAgentPrompt":"I have 4 join coverage records — row-1 on 2026-10-01 with source_rows=1 and matched_rows=10 in group-a, row-2 on 2026-10-02 with source_rows=2 and matched_rows=20 in group-b, row-3 on 2026-10-03 with source_rows=3 and matched_rows=15 in group-a, and row-4 on 2026-10-04 with source_rows=4 and matched_rows=30 in group-b — can you run the string normalisation and date-by-category pivot reports on the matched_rows field?","exampleUseCases":[{"title":"ETL pipeline join quality audit","prompt":"Here are 50 join coverage rows from last month's ETL run across group-alpha and group-beta, each with a date, source_rows, and matched_rows — can you normalise the strings and give me a pivot of matched_rows by date and dataset group?"},{"title":"Dataset matching rate breakdown by category","prompt":"I want to understand how well my data joins are performing across three categories — retail, wholesale, and online — over the past two weeks. I have 120 records with dates, source_rows, and matched_rows. Can you run the string normalisation and date-by-category pivot on source_rows for me?"},{"title":"Daily join coverage trend for two teams","prompt":"We have join coverage logs for team-A and team-B from 2026-09-01 to 2026-09-30, about 80 rows total with matched_rows and source_rows filled in. Please run the two analytical reports — string normalisation plus date-category pivot — using matched_rows as the field."}],"resultDescription":"Returns two independent analytical reports: (1) a string-normalised view of the supplied join coverage records, and (2) a date-by-category pivot table aggregating the selected field (matched_rows or source_rows) across dataset groups and dates. Covers up to 200 rows per call with no external lookups.","failureModes":["More than 200 rows supplied — request rejected with validation error","Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) on any row — row rejected or error returned","Date not in YYYY-MM-DD format — schema validation failure","Invalid field selector (not matched_rows or source_rows) — enum validation error","Empty rows array — minItems constraint violation"],"whenToPreferThis":"Choose this endpoint when you have up to 200 join coverage records with matched_rows and source_rows metrics per dataset group and date, and need both string normalisation and a date-by-category pivot in a single low-cost call ($0.01 USDC). It is a self-contained analytical compute endpoint — ideal when you do not need live database lookups, external enrichment, or prediction, just local analytical reporting on data you already have.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T12:50:52.702Z","isFirstParty":false,"canonicalSlug":"join-coverage-analytics-string-normalisation-date-by-category-pivot-f121bb40"}