{"uid":"cap_F6dchiZx2hvFpIDCdqC69","slug":"join-coverage-analytics-record-quality-audit-string-normalisation-two-f7c68b62","name":"Join Coverage Analytics: Record Quality Audit + String Normalisation + Two-Measure Correlation","description":"Join coverage — Record quality audit + String normalisation + Two-measure correlation: Record quality audit + String normalisation + Two-measure correlation for supplied join coverage records (matched_rows, source_rows). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-204?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}}},"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_-37sOZGjlPbdEZAOCgrU9","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":"Runs three bundled analytical reports on up to 200 join coverage records (matched_rows, source_rows): record quality audit, string normalisation, and two-measure correlation.","exampleAgentPrompt":"Can you run a quality audit, string normalisation, and correlation analysis on my join coverage data? Here are my records: row-1 for group-a on 2026-10-01 had 10 matched_rows out of 1 source_rows, row-2 for group-b on 2026-10-02 had 20 matched_rows out of 2 source_rows, row-3 for group-a on 2026-10-03 had 15 matched_rows out of 3 source_rows.","exampleUseCases":[{"title":"ETL pipeline join quality check","prompt":"I've got join coverage results from my ETL pipeline — can you audit the record quality, normalise the group name strings, and show me the correlation between matched_rows and source_rows? I have about 50 rows across two dataset groups from last month."},{"title":"Detecting suspicious join ratios","prompt":"Some of my joined datasets look off — matched_rows is way higher than source_rows for certain groups. Can you run a quality audit and correlation report on these 30 join coverage records to flag anomalies?"},{"title":"Data engineering QA before reporting","prompt":"Before I publish my weekly data coverage report, can you run all three analytics — quality audit, string normalisation, and two-measure correlation — on my 120 join coverage records across groups alpha, beta, and gamma from October 2026?"}],"resultDescription":"Three independent analytical reports for the supplied rows: (1) a record quality audit flagging nulls, out-of-range values, and structural issues; (2) a string normalisation report cleaning and standardising text fields like dataset_group; (3) a two-measure correlation report quantifying the statistical relationship between matched_rows and source_rows across the dataset.","failureModes":["More than 200 rows submitted — request rejected with max items exceeded error","Missing required fields (row_id, date, dataset_group, matched_rows, source_rows) — validation error per row","Date strings not matching YYYY-MM-DD pattern — schema validation failure","Numeric values outside allowed range (±1 trillion) — rejected","Empty rows array — minItems violation"],"whenToPreferThis":"Choose this endpoint when you need a cost-effective, bundled set of three data quality checks on join coverage metrics without any external lookups or live data — especially useful for ETL pipeline QA, data engineering audits, or pre-reporting validation of matched vs source row counts across dataset groups. Best when you have up to 200 rows and want record quality, string hygiene, and correlation analysis in a single call for $0.01 USDC.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T18:54:29.691Z","isFirstParty":false,"canonicalSlug":"join-coverage-analytics-record-quality-audit-string-normalisation-two-f7c68b62"}