{"uid":"cap_RxBiHNlS2sQhEfdDFuxJW","slug":"join-coverage-analytics-numeric-interval-filter-two-measure-correlation-51f11c66","name":"Join Coverage Analytics: Numeric Interval Filter + Two-Measure Correlation","description":"Join coverage — Numeric interval filter + Two-measure correlation: Numeric interval filter + Two-measure correlation 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-09-131?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"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_PDxBwngeiRFAx8pbeTyS_","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":"Filters join coverage rows by a numeric interval on a chosen measure and computes correlation between matched_rows and source_rows for supplied dataset records.","exampleAgentPrompt":"Run a join coverage analysis on my dataset: I have rows with matched_rows and source_rows values, and I want to filter where matched_rows is between 5 and 150, then compute the correlation between matched_rows and source_rows across all my records — here are the rows: [{row_id: 'r1', date: '2025-01-10', dataset_group: 'group-a', matched_rows: 80, source_rows: 100}, {row_id: 'r2', date: '2025-01-11', dataset_group: 'group-b', matched_rows: 10, source_rows: 200}].","exampleUseCases":[{"title":"ETL pipeline join quality check","prompt":"I have join coverage data from my ETL pipeline with matched_rows and source_rows per day. Can you filter the records where matched_rows is between 10 and 500, and then compute the correlation between matched_rows and source_rows? Here's the data: [{row_id:'etl-1', date:'2025-03-01', dataset_group:'orders', matched_rows:120, source_rows:300}, {row_id:'etl-2', date:'2025-03-02', dataset_group:'orders', matched_rows:8, source_rows:310}]."},{"title":"Dataset group coverage comparison","prompt":"Can you analyze my join coverage for two dataset groups — 'customers' and 'transactions' — filtering on source_rows between 50 and 1000 and then telling me how correlated source_rows and matched_rows are across those records? My rows are: [{row_id:'c1', date:'2025-06-01', dataset_group:'customers', matched_rows:400, source_rows:500}, {row_id:'t1', date:'2025-06-01', dataset_group:'transactions', matched_rows:200, source_rows:600}]."},{"title":"Anomaly detection in join results","prompt":"I suspect some of my join results are anomalous. Can you filter my join coverage records where matched_rows is between 0 and 50 — which I consider suspect — and compute the correlation between matched_rows and source_rows so I can see if there's a pattern? Data: [{row_id:'row-a', date:'2025-09-15', dataset_group:'inventory', matched_rows:30, source_rows:900}, {row_id:'row-b', date:'2025-09-16', dataset_group:'inventory', matched_rows:45, source_rows:850}, {row_id:'row-c', date:'2025-09-17', dataset_group:'inventory', matched_rows:200, source_rows:820}]."}],"resultDescription":"Returns two analytical reports: (1) rows from the input dataset that fall within the specified numeric interval for the chosen field (matched_rows or source_rows), and (2) a correlation analysis between matched_rows and source_rows across the supplied records. Results are based solely on the supplied data — no external lookups or live data.","failureModes":["Input array is empty or missing — returns validation error","Rows exceed 200 item limit — request rejected","minimum greater than maximum — may return empty filtered set or validation error","Missing required fields (row_id, date, matched_rows, source_rows, dataset_group) — schema validation failure","Null values in both measure fields — correlation computation may be undefined or return NaN","Date field not matching YYYY-MM-DD pattern — schema validation error"],"whenToPreferThis":"Use this endpoint when you have pre-collected join coverage data (matched_rows and source_rows per record) and need both a numeric range filter and a correlation analysis in a single $0.01 call. Ideal for data engineers and analytics pipelines that want to QA join quality, detect anomalies, or measure the statistical relationship between source and matched row counts without sending data to an external live system. Prefer this over general-purpose analytics tools when you need offline, deterministic computation on up to 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T18:54:14.216Z","isFirstParty":false,"canonicalSlug":"join-coverage-analytics-numeric-interval-filter-two-measure-correlation-51f11c66"}