{"uid":"cap_1ic3vsVsRBMGR8_9oAWqs","slug":"vextorium-csv-cleaner-normalizer-c0050b7f","name":"Vextorium CSV Cleaner & Normalizer","description":"CSV cleaning and normalization to JSON: column names, types, duplicates and empty values. Data is processed in memory and not stored.","url":"https://api.vextorium.com/limpiar-datos-pago?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":{"required":["input","from","to"],"properties":{"to":{"enum":["json"],"type":"string","description":"Formato de salida"},"from":{"enum":["csv"],"type":"string","description":"Formato de entrada"},"input":{"type":"string","description":"Contenido crudo del CSV a limpiar"}}},"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","properties":{"datos":{"type":["array","null"],"items":{"type":"object","additionalProperties":true},"description":"Cleaned rows; each row is an object whose keys are the (normalized) CSV column names"},"filas":{"type":["number","null"]},"columnas":{"type":["array","null"],"items":{"type":["string","null"]}},"aviso_privacidad":{"type":["string","null"]},"cambios_aplicados":{"type":["array","null"],"items":{"type":["string","null"]}}}}}}}},"responseSchema":{"type":"json","example":{"datos":[{"edad":25,"nombre":"Ana"}],"filas":2,"columnas":["nombre","edad"],"aviso_privacidad":"Estos datos se procesan en memoria y no se almacenan.","cambios_aplicados":["Nombres de columnas normalizados"]}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.02","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":"$0.02/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.02","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.02","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_6Wdrx5nHbeSg4TCt__QEB","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.02","costPer":"request","priority":0,"asset":null,"unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Cleans and normalizes raw CSV data in-memory, returning structured JSON with normalized column names, inferred types, deduplicated rows, and a changelog of applied transformations.","exampleAgentPrompt":"Take this raw CSV text and clean it up for me — normalize the column names, fix the data types, remove any duplicate or empty rows, and give me back clean JSON records: 'Name,Age,Email\\nAlice,29,alice@example.com\\nAlice,29,alice@example.com\\nBob,,bob@example.com'","exampleUseCases":[{"title":"Cleaning a dirty spreadsheet export","prompt":"I exported this CSV from our CRM and it's a mess — duplicate rows, inconsistent column names, and missing values all over the place. Can you clean it up and give me back the data as JSON? Here's the raw CSV: 'customer_name,AGE,Email Address\\nJane Doe,34,jane@acme.com\\nJane Doe,34,jane@acme.com\\nJohn,,john@acme.com'"},{"title":"ETL pipeline data normalization","prompt":"Before I load this CSV into our database, I need it cleaned and normalized into JSON — fix the column names, drop duplicates, infer proper data types, and tell me what changes were made. Here's the CSV: 'Product Name,Price ,Qty,Category\\nWidget A,9.99,10,tools\\nWidget A,9.99,10,tools\\nGadget B,,5,electronics'"},{"title":"Validating user-uploaded CSV files","prompt":"A user just uploaded this CSV file to our platform and I need to validate and clean it before processing — normalize the headers, remove empty rows, deduplicate, and return it as a JSON array so I can work with it programmatically. Raw CSV: 'First Name,Last Name ,email,Phone Number\\nMaria,Garcia,maria@email.com,555-1234\\n,Smith,smith@email.com,555-5678\\nMaria,Garcia,maria@email.com,555-1234'"}],"resultDescription":"Returns a JSON object with: 'datos' (array of cleaned row objects with normalized column names as keys), 'filas' (number of rows after cleaning), 'columnas' (list of normalized column names), 'cambios_aplicados' (array of strings describing each transformation applied), and 'aviso_privacidad' (privacy notice confirming data is not stored).","failureModes":["Malformed or non-parseable CSV input returns an error — input must be valid CSV text","Missing required fields (input, from, to) cause a 400-level validation error","Very large CSV payloads may time out or hit memory limits","Passing non-CSV data in the 'input' field when 'from' is set to 'csv' will produce empty or erroneous output","Incorrect bodyType specification may cause parsing failures"],"whenToPreferThis":"Choose this endpoint when you need lightweight, in-memory CSV cleaning and JSON conversion without storing data — ideal for ETL preprocessing, validating user-uploaded CSVs before ingestion, or normalizing spreadsheet exports before further processing. It is especially useful when you need a changelog of applied transformations (duplicates removed, empty values handled, column names normalized) returned alongside the clean data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-01T00:47:39.794Z","isFirstParty":false,"canonicalSlug":"vextorium-csv-cleaner-normalizer-c0050b7f"}