{"uid":"cap_AEc88kbNfh0hZ6ka830Ie","slug":"join-coverage-analytics-record-quality-audit-string-normalisation-ed24299a","name":"Join Coverage Analytics: Record Quality Audit + String Normalisation + Exact Row Deduplication","description":"Join coverage — Record quality audit + String normalisation + Exact row deduplication: Record quality audit + String normalisation + Exact row deduplication 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-09-191?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_i2p4NJ1RbDd1I2k-bxYUH","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":"Audits record quality, normalises strings, and deduplicates rows for up to 200 join coverage records (matched_rows, source_rows) in three independent local analytical reports.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and exact row deduplication on my join coverage data? I have rows with row_id, dataset_group, date (YYYY-MM-DD), matched_rows, and source_rows — here are up to 200 records I'd like all three reports for.","exampleUseCases":[{"title":"ETL pipeline join coverage validation","prompt":"I've just run an ETL join and have a table of up to 150 records with columns row_id, dataset_group, date, matched_rows, and source_rows — can you audit the record quality, normalise any messy strings, and flag exact duplicate rows so I can trust the join coverage results?"},{"title":"Data quality check before dashboard publish","prompt":"Before I publish this join coverage summary to the dashboard, can you run all three checks — record quality audit, string normalisation, and duplicate row detection — on these 80 join coverage records with their matched_rows and source_rows counts?"},{"title":"Deduplicating joined dataset exports","prompt":"I exported a join coverage report with around 60 rows that might have duplicates and inconsistent string formatting across the dataset_group field — can you give me the deduplication report and string normalisation output alongside a full quality audit?"}],"resultDescription":"Three independent analytical reports covering: (1) a record quality audit flagging data issues in the join coverage records, (2) a string normalisation report cleaning and standardising text fields like dataset_group, and (3) an exact row deduplication report identifying and removing fully duplicate rows — all computed locally on the supplied data with no external lookups.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) on any row — validation error","Invalid date format (not YYYY-MM-DD) — schema validation failure","row_id or dataset_group exceeding 80 character limit — schema validation failure","matched_rows or source_rows outside allowed numeric range — schema validation failure","Empty rows array — minItems:1 violation"],"whenToPreferThis":"Choose this endpoint when you need three combined data-quality operations — record quality audit, string normalisation, and exact row deduplication — applied together to join coverage records in a single cost-effective call. It is ideal for data engineers validating ETL join outputs, analysts cleaning join coverage exports before reporting, or pipelines that need lightweight local analytics without any external API lookups or predictions. Prefer this over single-report endpoints when all three operations are needed simultaneously and your dataset is 200 rows or fewer.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:44.548Z","isFirstParty":false,"canonicalSlug":"join-coverage-analytics-record-quality-audit-string-normalisation-ed24299a"}