{"uid":"cap_xeV6z5nICgIcpbbfB56AB","slug":"vextorium-text-classification-2c363c6d","name":"Vextorium Text Classification","description":"552 pay-per-call data APIs for AI agents: Solana and EVM on-chain data, tokenized stocks, DeFi, Hyperliquid, economy and official statistics, FX, validation, web and developer tools. Pay in USDC via x402.","url":"https://api.vextorium.com/v1/classify-text?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"text":{"type":"string","description":"Text to classify"},"labels":{"type":"array","items":{"type":"string"},"description":"2-30 candidate labels"},"multiple":{"type":"boolean","description":"Allow several labels (default false)"}}},"responseSchema":{"type":"json","example":{"model":"Qwen3-4B (local)","labels":["reclamacion"],"reason":"El texto expresa un problema con una orden y falta de respuesta, lo cual son elementos ...","confidence":0.95}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.005","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":"$0.005/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.005","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.005","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_4F-PxAdnAVJMt1y7wbm3h","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.005","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Classifies a piece of text into one or more user-provided candidate labels using a local LLM, returning matched labels, a confidence score, and a reasoning explanation.","exampleAgentPrompt":"Classify this support message into one of these labels — 'complaint', 'refund_request', 'general_inquiry', or 'compliment' — and tell me how confident you are: 'I've been waiting 3 weeks for my order and nobody is responding to my emails.'","exampleUseCases":[{"title":"Customer support ticket routing","prompt":"Classify this incoming support ticket into one of these categories: 'billing', 'technical_issue', 'shipping', or 'account_access'. The message is: 'My payment went through but my account still shows as unpaid and I can't access premium features.'"},{"title":"Content moderation intent detection","prompt":"I need to know whether this user comment falls under 'hate_speech', 'spam', 'off_topic', or 'acceptable'. The comment is: 'Buy followers fast — click here now!!! Limited offer!' Allow only one label."},{"title":"Multi-label news article tagging","prompt":"Tag this news excerpt with all that apply from: 'economy', 'geopolitics', 'tech', 'climate', 'health'. Allow multiple labels. The text is: 'Central banks worldwide are raising rates while green energy subsidies face cuts amid slowing global growth.'"}],"resultDescription":"Returns a JSON object with the model name used (e.g. 'Qwen3-8B (local)'), an array of matched labels, a natural-language reason explaining why those labels were chosen, and a confidence score between 0 and 1.","failureModes":["Fewer than 2 or more than 30 candidate labels provided — validation error","Text field is missing or empty — required field error","Label array is missing — required field error","Ambiguous text leads to low confidence score with unexpected label assignment","Non-English text may reduce accuracy depending on model language support","Payment failure via x402 results in 402 response before processing"],"whenToPreferThis":"Choose this endpoint when you need zero-shot text classification with a natural language explanation and confidence score, without needing to train a custom model. Ideal for dynamic label sets defined at call time (e.g. support categories, intent buckets, topic tags). Prefer this over embedding-based classifiers when interpretability (the 'reason' field) matters. The pay-per-call x402 model makes it suitable for sporadic or agent-driven workloads without subscription overhead.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-12T18:49:10.482Z","isFirstParty":false,"canonicalSlug":"vextorium-text-classification-2c363c6d"}