{"uid":"cap_fgzQjyZ2RqZgpS34NDegE","slug":"agentstoolshub-csv-validator-0430e939","name":"AgentsToolsHub CSV Validator","description":"Validate CSV structure before ingesting it: RFC 4180-aware parse, ragged rows, column-count consistency against the header or an expected schema, duplicate headers, stray quotes and empty-cell counts. Deterministic — every issue cites its exact row number. Use this when an agent needs to structural lint: ragged rows, dupes, quoting issues.","url":"https://agentstoolshub.com/api/v1/dev/csv-validator?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method"],"properties":{"type":{"type":"string","const":"http"},"method":{"enum":["GET"],"type":"string"},"queryParams":{"type":"object","required":["csv"],"properties":{"csv":{"type":"string","description":"CSV text"},"delimiter":{"enum":[",",";","tab"],"type":"string","description":"Delimiter"},"expectedColumns":{"type":"string","description":"Expected columns"}}}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object"}}}}},"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_laT9dSYcV9bSGtuXUNnAP","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 CSV text for structural issues including ragged rows, duplicate headers, stray quotes, column-count mismatches, and empty cells, with exact row-number citations for every issue found.","exampleAgentPrompt":"Can you check this CSV for structural problems — ragged rows, duplicate headers, stray quotes, or column-count mismatches? The delimiter is a comma and I'm expecting columns: id, name, email, created_at.","exampleUseCases":[{"title":"Pre-ingestion ETL data quality check","prompt":"Before we load this CSV into the database, can you validate it for any structural issues — ragged rows, duplicate headers, or stray quotes? The file uses a semicolon delimiter and the expected columns are product_id, sku, price, stock_count."},{"title":"User-uploaded file validation","prompt":"A user just uploaded this CSV to our platform. Can you lint it for formatting problems like mismatched column counts or empty cells and give me the exact row numbers where issues appear? Delimiter is comma."},{"title":"Automated data pipeline integrity check","prompt":"We're about to process this CSV export from our CRM. Can you check it against the expected columns — contact_id, first_name, last_name, email, phone — and flag any rows with ragged columns, duplicate headers, or quoting errors?"}],"resultDescription":"A structured list of validation issues found in the CSV, each citing the exact row number and describing the problem (e.g. ragged row, duplicate header, stray quote, empty cell, column count mismatch). Returns a clean result if no issues are found.","failureModes":["Missing required 'csv' query parameter returns an error","Extremely large CSV payloads may exceed size limits","Invalid delimiter value (not comma, semicolon, or tab) returns a bad request error","Malformed expectedColumns parameter may cause schema comparison to fail","Network timeout if CSV text is very long"],"whenToPreferThis":"Use this endpoint when an agent needs to structurally lint a CSV before ingesting, transforming, or storing it. It is ideal for pre-flight checks in ETL pipelines, validating user-uploaded files, or confirming that a CSV export matches an expected schema. Prefer this over general-purpose parsers when you need exact row-number citations for every structural issue and RFC 4180-aware parsing including ragged row and duplicate header detection.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T12:34:19.428Z","isFirstParty":false,"canonicalSlug":"agentstoolshub-csv-validator-0430e939"}