{"uid":"cap_vbT02FeUttijJa7Asn8l2","slug":"sampling-observations-string-normalisation-numeric-interval-filter-566b2b22","name":"Sampling Observations — String Normalisation + Numeric Interval Filter","description":"Sampling observations — String normalisation + Numeric interval filter: String normalisation + Numeric interval filter for supplied sampling observations records (sample_value, sample_weight). 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-15-026?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","sample_value","sample_weight"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"sample_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"sample_weight":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","sample_value":10,"dataset_group":"group-a","sample_weight":1},{"date":"2026-10-02","row_id":"row-2","sample_value":20,"dataset_group":"group-b","sample_weight":2},{"date":"2026-10-03","row_id":"row-3","sample_value":15,"dataset_group":"group-a","sample_weight":3},{"date":"2026-10-04","row_id":"row-4","sample_value":30,"dataset_group":"group-b","sample_weight":4}],"maxItems":200,"minItems":1},"field":{"enum":["sample_value","sample_weight"],"type":"string","default":"sample_value"},"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_nmzPPTOYSiuYUWKhpagl9","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 filters sampling observation rows (sample_value or sample_weight) within a specified numeric interval, producing 2 analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you run string normalisation and a numeric interval filter on my sampling observations data — filter on sample_value between 0 and 100 — here are my rows: row-1 (group-a, 2026-10-01, value=10, weight=1), row-2 (group-b, 2026-10-02, value=20, weight=2), row-3 (group-a, 2026-10-03, value=15, weight=3), row-4 (group-b, 2026-10-04, value=30, weight=4)?","exampleUseCases":[{"title":"Survey sample quality control","prompt":"I have 150 survey sample records with dataset groups and sample_value scores — can you normalise the group name strings and filter to only keep rows where sample_value is between 5 and 95, then give me the two analytical reports?"},{"title":"Weight-based outlier exclusion","prompt":"Filter my sampling observations to only those with sample_weight between 0.5 and 10, and normalise the dataset_group strings — I've got about 80 rows across two groups collected between 2026-01-01 and 2026-06-30."},{"title":"Clinical trial data cleanup","prompt":"I need to clean up my clinical trial sampling data: normalise all the string fields in dataset_group and keep only the rows where sample_value falls between 50 and 200 — here are my 120 patient observation rows."}],"resultDescription":"Returns 2 independent analytical reports covering: (1) string-normalised versions of text fields (e.g. dataset_group), and (2) rows filtered to those whose chosen numeric field (sample_value or sample_weight) falls within the specified [minimum, maximum] interval. Results are computed locally with no external lookups. Up to 200 input rows supported per call.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Invalid date format (not YYYY-MM-DD) — validation error","Minimum value greater than maximum value — may return empty filtered set or error","Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) — schema validation failure","sample_value or sample_weight outside allowed range (-1e12 to 1e12) — schema rejection","Payment not completed via x402 protocol — 402 response, no report returned"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation of categorical/text fields and numeric range filtering applied to sampling observation records in a single call, and your dataset is 200 rows or fewer. It is purpose-built for survey, experimental, or monitoring sample data with explicit sample_value and sample_weight fields. Prefer this over general-purpose data tools when you want two bundled local analytical reports without any external data lookups, predictions, or live integrations.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:54:37.359Z","isFirstParty":false,"canonicalSlug":"sampling-observations-string-normalisation-numeric-interval-filter-566b2b22"}