{"uid":"cap_YnzVedQRlDO9PLaNI5rHN","slug":"join-coverage-record-quality-audit-string-normalisation-a2b72a16","name":"Join Coverage — Record Quality Audit + String Normalisation","description":"Join coverage — Record quality audit + String normalisation: Record quality audit + String normalisation 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-09-001?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_vRLWmQ0J7iy9aADO_eepc","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 a record quality audit and string normalisation analysis on up to 200 join coverage rows (matched_rows, source_rows), returning 2 independent analytical reports.","exampleAgentPrompt":"Can you run a record quality audit and string normalisation check on my join coverage data? I have 4 rows with dataset groups 'group-a' and 'group-b', dates from 2026-10-01 to 2026-10-04, and matched_rows/source_rows values like 10/1, 20/2, 15/3, 30/4.","exampleUseCases":[{"title":"ETL pipeline join coverage QA","prompt":"I've just run a join between two tables and want to audit the quality of the join results — can you check these 50 rows of matched_rows and source_rows values across my dataset groups and flag any quality issues or string normalisation problems?"},{"title":"Data team join ratio anomaly check","prompt":"We have weekly join coverage metrics from our data warehouse — can you run a record quality audit on these 120 rows (each with a date, group name, matched_rows, and source_rows) and tell me if there are any string inconsistencies or data quality red flags?"},{"title":"Pre-reporting dataset validation","prompt":"Before I hand off this join coverage report to stakeholders, can you audit the quality of the 80 records I have — checking matched_rows and source_rows across groups like 'sales' and 'marketing' — and normalise any messy strings in the dataset?"}],"resultDescription":"Two independent analytical reports: (1) a record quality audit covering anomalies, completeness, and consistency across the supplied join coverage rows; (2) a string normalisation report identifying and correcting string formatting issues in fields like dataset_group and row_id. Both reports are computed locally against the supplied data with no external lookups.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) causes validation error","Invalid date format (not YYYY-MM-DD) triggers schema validation failure","Numeric values outside allowed range (-1e12 to 1e12) rejected","Empty rows array (minItems: 1 not met) causes error","Non-numeric or non-null matched_rows/source_rows values rejected"],"whenToPreferThis":"Choose this endpoint when you need both a record quality audit and string normalisation analysis on join coverage data (matched_rows vs source_rows) in a single low-cost call. It is ideal for data engineers and analysts running post-join QA checks on small-to-medium batches (up to 200 rows) without needing live lookups, predictions, or external data enrichment. Prefer it over broader analytics endpoints when your primary concern is join coverage integrity and string consistency.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:41.323Z","isFirstParty":false,"canonicalSlug":"join-coverage-record-quality-audit-string-normalisation-a2b72a16"}