{"uid":"cap_ZzW-CMkkNIBEqDgRjYxld","slug":"csv-import-preflight-validator-5ba09402","name":"CSV Import Preflight Validator","description":"Check up to 500 supplied CSV records for quoting, column counts, duplicate headers, spreadsheet-formula prefixes and caller-declared scalar types. Return bounded row/column findings and input integrity hashes. No cell execution, automatic repair, PII detection or import-success guarantee.","url":"https://www.mahastrategies.com/api/v1/demand/csv-import-preflight?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method","bodyType","body"],"properties":{"body":{"type":"object","required":["dataClass","csv","delimiter","columns"],"properties":{"csv":{"type":"string","maxLength":24000,"minLength":1},"columns":{"type":"array","maxItems":64,"minItems":1},"dataClass":{"enum":["public","synthetic"],"type":"string"},"delimiter":{"enum":[",",";","\t"],"type":"string"}},"additionalProperties":false},"type":{"type":"string","const":"http"},"method":{"enum":["POST"],"type":"string"},"bodyType":{"enum":["json","form-data","text"],"type":"string"}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object","required":["version","productId","inputHash","resultHash","receiptDigest","result","limitations"],"properties":{"result":{"type":"object"},"version":{"type":"string","const":"maha-demand6/1.0.0"},"inputHash":{"type":"string"},"productId":{"type":"string","const":"csv-import-preflight"},"resultHash":{"type":"string"},"limitations":{"type":"array","maxItems":16,"minItems":1},"receiptDigest":{"type":"string"}},"additionalProperties":false}}}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.0135","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"registry","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":"$0.0135/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.0135","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.0135","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_UuFlkNxEhCH_I4X_M2Ukc","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.0135","costPer":"request","priority":0,"asset":null,"unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Validates up to 500 CSV records for quoting errors, column count mismatches, duplicate headers, spreadsheet-formula injection prefixes, and declared scalar type conformance before import.","exampleAgentPrompt":"Before I load this CSV into the database, run a preflight check on it — it uses comma delimiters, I've declared three columns (name, age, email), the data class is synthetic, and I need you to flag any quoting errors, duplicate headers, formula injection prefixes, or type mismatches.","exampleUseCases":[{"title":"ETL pipeline safety gate","prompt":"I'm about to run an ETL job on this CSV. Can you preflight-check it first? It's comma-delimited, the declared columns are user_id, signup_date, and plan_tier, the data class is public, and I want to catch any quoting issues, column count problems, or formula injection prefixes before anything gets imported."},{"title":"User-uploaded CSV threat screening","prompt":"A user just uploaded a CSV to our platform and I need to check it for spreadsheet formula injection attacks and structural issues before we process it. It's semicolon-delimited, data class is public, and the declared columns are product_name, price, and quantity — can you run the preflight check and tell me what's wrong?"},{"title":"Data science notebook import check","prompt":"I have a tab-delimited CSV with synthetic data that I want to load into a notebook. The declared columns are experiment_id, measurement, and result. Can you run a preflight validation to make sure there are no duplicate headers, bad quoting, or type mismatches before I import it?"}],"resultDescription":"Returns a structured object containing row- and column-level findings (quoting errors, column count mismatches, duplicate headers, formula-prefix detections, scalar type violations), an input integrity hash, a result hash, a receipt digest, a product and version identifier, and a list of limitations (e.g. no PII detection, no cell execution, no automatic repair).","failureModes":["CSV string exceeds 24,000 character limit — request rejected","columns array is empty or exceeds 64 items — validation error","invalid delimiter value (must be comma, semicolon, or tab) — schema error","dataClass not one of 'public' or 'synthetic' — enum validation failure","malformed JSON body — 400 bad request","CSV contains more than 500 rows — truncated or rejected","missing required fields in body — validation error"],"whenToPreferThis":"Use this endpoint when you need a lightweight, deterministic structural check on CSV data before committing it to an import pipeline, database, or ETL process — especially when you want formula-injection detection, integrity hashes for audit trails, or column-type conformance checks without executing any cell content or modifying the data. Prefer this over full ETL tools when the goal is pre-import validation only, not transformation or repair.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-17T06:52:41.304Z","isFirstParty":false,"canonicalSlug":"csv-import-preflight-validator-5ba09402"}