{"uid":"cap_36q-BGq3UTwAVWnMtgFPg","slug":"schema-conformity-string-normalisation-numeric-interval-filter-acc61e93","name":"Schema Conformity — String Normalisation + Numeric Interval Filter","description":"Schema conformity — String normalisation + Numeric interval filter: String normalisation + Numeric interval filter for supplied schema conformity records (valid_records, checked_records). 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-02-026?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","valid_records","checked_records"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"valid_records":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"checked_records":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","valid_records":10,"checked_records":1},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","valid_records":20,"checked_records":2},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","valid_records":15,"checked_records":3},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","valid_records":30,"checked_records":4}],"maxItems":200,"minItems":1},"field":{"enum":["valid_records","checked_records"],"type":"string","default":"valid_records"},"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_p9-KYvAEppd918ov3g95l","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":"Normalises string fields and applies a numeric interval filter to schema conformity records (valid_records, checked_records), returning up to 2 analytical reports for up to 200 rows.","exampleAgentPrompt":"I have up to 200 schema conformity rows with valid_records and checked_records fields — can you run string normalisation and then filter the valid_records to only rows between 10 and 500, across groups like 'group-a' and 'group-b' dated from 2026-10-01 onward?","exampleUseCases":[{"title":"Data quality pipeline pre-check","prompt":"I've got 150 schema conformity records from our ETL pipeline — can you normalize the string fields and filter checked_records to only those between 0 and 1000, so I can see which rows are within a valid range?"},{"title":"Audit of valid record counts by group","prompt":"Run string normalisation and a numeric interval filter on my dataset — I want to focus on the valid_records field for values between 5 and 200, and my rows are tagged with dataset groups like 'finance' and 'ops' from dates in late 2026."},{"title":"Spot-check conformity metrics before reporting","prompt":"Before I finalize my data quality report, can you normalize string fields and filter valid_records between 50 and 10000 on these 80 schema conformity rows so I can catch any outliers or formatting issues?"}],"resultDescription":"Returns two independent analytical reports: one covering string normalisation of the specified fields across submitted rows, and one covering a numeric interval filter applied to the chosen field (valid_records or checked_records) within the given minimum–maximum bounds. Covers up to 200 input rows, processed locally with no external lookups or live data.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Invalid date format (not YYYY-MM-DD) causes row-level validation failure","Minimum value greater than maximum value may produce empty filter results","Missing required fields (row_id, valid_records, checked_records) causes row rejection","Field enum value not one of 'valid_records' or 'checked_records' causes parameter error","Null numeric values in the filtered field may be excluded from interval results"],"whenToPreferThis":"Choose this endpoint when you need offline, local analytical processing of schema conformity data — specifically when you want both string normalisation and numeric interval filtering in a single call for up to 200 rows. It is ideal for data quality pipelines where you need to clean string representations and isolate records within a specific numeric range of valid_records or checked_records, without any live lookup or external data enrichment.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-11T12:48:39.634Z","isFirstParty":false,"canonicalSlug":"schema-conformity-string-normalisation-numeric-interval-filter-acc61e93"}