{"uid":"cap_vsvpx0FS3jsko4dcnZNhP","slug":"join-coverage-analytics-record-quality-audit-string-normalisation-9631b82e","name":"Join Coverage Analytics — Record Quality Audit, String Normalisation & Grouped Numeric Summary","description":"Join coverage — Record quality audit + String normalisation + Grouped numeric summary: Record quality audit + String normalisation + Grouped numeric summary 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-09-194?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_ivpE6CUTrrp4GA-kABBu4","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 offline analytical reports on join coverage data (matched_rows, source_rows): a record quality audit, string normalisation, and grouped numeric summary — up to 200 rows per call.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and grouped numeric summary on my join coverage data? I have 4 rows with dataset groups 'group-a' and 'group-b', matched_rows ranging from 10 to 30, and source_rows from 1 to 4 — use matched_rows as the primary field.","exampleUseCases":[{"title":"Data pipeline join coverage audit","prompt":"I've just run a join between two datasets and want to audit the quality — can you check these 10 join coverage records for data issues, normalise the group name strings, and give me a grouped numeric summary of matched_rows across my dataset groups?"},{"title":"Detecting match rate anomalies by group","prompt":"I have join coverage records from last week with matched_rows and source_rows for groups 'transactions' and 'users' — can you run a quality audit and grouped numeric summary so I can spot which groups have the worst match rates?"},{"title":"Pre-ingestion data validation","prompt":"Before I ingest these 50 join coverage rows into our warehouse, can you run a record quality audit and string normalisation on the dataset_group labels, and summarise the source_rows numeric stats by group?"}],"resultDescription":"Returns three independent analytical reports for the supplied join coverage rows: (1) a record quality audit flagging missing values, anomalies, and completeness issues; (2) a string normalisation report cleaning dataset_group and other string fields; (3) a grouped numeric summary (e.g. counts, means, totals) of the selected numeric field (matched_rows or source_rows) broken down by dataset_group. All computed locally with no external lookups.","failureModes":["Submitting more than 200 rows causes rejection or truncation","Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) causes validation errors","Invalid date format (non YYYY-MM-DD) causes schema rejection","Numeric values outside allowed range cause validation failure","Empty rows array returns an error"],"whenToPreferThis":"Choose this endpoint when you need three combined analytical operations on join coverage data in a single call — quality auditing, string normalisation, and grouped numeric summarisation — without making external API calls or requiring live data. Ideal for data engineering pipelines that need lightweight, offline validation and summarisation of match/source row metrics grouped by dataset category.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-11T12:48:34.047Z","isFirstParty":false,"canonicalSlug":"join-coverage-analytics-record-quality-audit-string-normalisation-9631b82e"}