{"uid":"cap_ah40QyVRGo48QVRrtUYXa","slug":"mx-data-helper-text-cleaner-14d4e60e","name":"MX Data Helper – Text Cleaner","description":"Pay-per-call x402 data services on Base mainnet","url":"https://temporary-fleet-eclipse-duogf0d.vercel.app/v1/text_clean","method":"POST","headers":{},"bodySchema":{"type":"object","required":["text"],"properties":{"text":{"type":"string","description":"Raw text to clean"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.001","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":"$0.001/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_fLsqPB8FBItWP5607RlHS","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.001","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Cleans and normalizes raw text by removing noise, formatting artifacts, or unwanted characters via a pay-per-call API on Base mainnet","exampleAgentPrompt":"Can you clean this raw text for me — strip out all the noise and formatting artifacts so it's ready to use: 'H3llo   W0rld!!  \n\n  This is meSsy   text...'?","exampleUseCases":[{"title":"Scrape cleanup before storage","prompt":"I just scraped a bunch of product descriptions from a website and they're full of weird whitespace, broken characters, and HTML artifacts. Can you clean this raw text so it's ready to save to our database: '<p>  Fr3sh   Appl3s!!  \n\r\n  100% Organic   </p>'?"},{"title":"Normalizing user-submitted form input","prompt":"A user just submitted this feedback form entry and it's got a ton of noise in it — extra spaces, weird punctuation, and stray newlines. Can you clean it up before I send it to our analytics pipeline: 'Gr8 product!!!   Really   loved   it\n\n\n...'?"},{"title":"Preprocessing text for NLP pipeline","prompt":"I need to run sentiment analysis on this customer review but first I want to clean up all the formatting junk and irregular characters so the model gets clean input. Here's the raw text: '  This product is   AMAZNG!!!   \t\t\nWould   buy again... 100%%.'"}],"resultDescription":"A cleaned, normalized version of the input text string, with noise, formatting artifacts, irregular whitespace, and unwanted characters removed, ready for downstream use or storage.","failureModes":["Missing required 'text' field returns a 400 or validation error","Empty string input may return an error or unchanged empty string","Payment failure on Base mainnet results in a 402 Payment Required response blocking the call","Very large text inputs may be rejected or time out","Network or Vercel deployment downtime returns 5xx errors"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, pay-per-call text cleaning operation without committing to a subscription-based NLP service. Ideal for preprocessing pipelines, scraping workflows, or sanitizing user inputs where cost-per-call economics matter and you want on-chain micropayment settlement via x402 on Base mainnet.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-16T00:30:24.020Z","isFirstParty":false}