{"uid":"cap_IK747tdnGEAAGkliyd8bW","slug":"evaluation-run-imports-numeric-type-check-70e57b5f","name":"Evaluation Run Imports — Numeric Type Check","description":"Evaluation run imports — Numeric type check: Numeric type check for Evaluation run imports. Validate a declared CSV table contract, up to 1000 rows. Quoted cells supported; no external enrichment. Specialised preset in a shared 50-profile, 20-operation engine. 0.01 USDC includes one CSV numeric type check report.","url":"https://market.datapackvibe.com/x402/assistant-bx-csv-evaluations-types?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"csv":{"type":"string","default":"evaluation_id,evaluated_on,dataset_id,correct_count,example_count\nrecord-a,2026-01-01,group-a,10,2\nrecord-b,2026-01-02,group-b,20,3","maxLength":100000,"minLength":1},"maximum":{"type":"number","default":1000000000000,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":-1000000000000,"maximum":1000000000000,"minimum":-1000000000000},"delimiter":{"enum":[",",";","\t"],"type":"string","default":","},"max_cell_length":{"type":"integer","default":1000,"maximum":5000,"minimum":1},"allowed_categories":{"type":"array","items":{"type":"string","default":"","maxLength":100,"minLength":1},"default":["group-a","group-b"],"maxItems":100,"minItems":0}}},"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_6WXOxizqhJbzelnWQ6ntL","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":"Validates that all numeric columns in an evaluation run CSV conform to expected numeric types, ranges, and category constraints, returning a type-check report.","exampleAgentPrompt":"Check the numeric types in my evaluation run CSV — it has columns evaluation_id, evaluated_on, dataset_id, correct_count, and example_count — make sure the numeric columns are valid, values are between 0 and 1000, and the allowed category values are group-a and group-b.","exampleUseCases":[{"title":"Pre-import ML evaluation data check","prompt":"Before I import this evaluation run CSV into our pipeline, can you validate that all numeric columns like correct_count and example_count are actually numbers, are between 0 and 10000, and that dataset_id only contains allowed values like group-a, group-b, or group-c?"},{"title":"Catching bad data in evaluation export","prompt":"I exported evaluation results to a CSV and I need to make sure none of the numeric fields got corrupted — can you run a numeric type check on it using comma as the delimiter and flag anything outside the range -1000 to 1000000?"},{"title":"Automated data quality gate for evaluation pipeline","prompt":"As part of our automated evaluation pipeline, check this CSV for numeric type violations before we proceed — the allowed category labels are experiment-1, experiment-2, and experiment-3, and numeric values should stay between 0 and 100."}],"resultDescription":"A numeric type check report covering up to 1000 rows of the submitted CSV, identifying which cells or columns fail numeric type expectations, fall outside the specified min/max range, exceed cell length limits, or contain disallowed category values. The report is scoped to numeric column validation only and does not perform external data enrichment.","failureModes":["CSV exceeds 100,000 character limit — request rejected","More than 1000 rows submitted — only first 1000 may be evaluated","Invalid delimiter specified — must be comma, semicolon, or tab","Minimum value exceeds maximum value — configuration error","Cell length exceeds max_cell_length limit — cell flagged or rejected","Malformed CSV structure — unparseable rows cause partial or failed report","Empty CSV submitted — validation cannot proceed"],"whenToPreferThis":"Choose this endpoint when you need to validate numeric column types and value ranges in an evaluation run CSV before import, particularly when your data follows the evaluation_id/evaluated_on/dataset_id/correct_count/example_count schema pattern. It is purpose-built for evaluation run import contracts, supports quoted cells, configurable delimiters, category allowlists, and min/max range bounds — making it ideal as a lightweight, low-cost data quality gate in ML evaluation pipelines.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:47:19.688Z","isFirstParty":false,"canonicalSlug":"evaluation-run-imports-numeric-type-check-70e57b5f"}