{"uid":"cap_WEwTR_7XEP6b_TcPXzb4H","slug":"agent402-tools-text-embedding-openai-text-embedding-3-small-46b77f19","name":"agent402.tools Text Embedding (OpenAI text-embedding-3-small)","description":"Generate a text embedding vector using OpenAI text-embedding-3-small (1536 dimensions). Ideal for semantic search, RAG, and clustering. No API key needed; pay per call via x402. Text capped at 32k chars.","url":"https://agent402.tools/api/embed","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"text":{"type":"string","description":"Text to embed (max 32,000 chars)"}}},"responseSchema":{"type":"json","example":{"model":"text-embedding-3-small","usage":{"total_tokens":12},"provider":"openai","embedding":[0.0023,-0.0091,0.0152],"dimensions":1536}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.002","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":"$0.002/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_MchZWACEM2zPyR120p6ru","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.002","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Generates a 1536-dimension text embedding vector using OpenAI text-embedding-3-small, paid per call via x402 with no API key required.","exampleAgentPrompt":"Turn this product description into a 1536-dim embedding vector so I can store it in my vector database for semantic search: 'Lightweight waterproof hiking boots with anti-slip soles and breathable mesh lining, ideal for trail use.'","exampleUseCases":null,"resultDescription":"A 1536-dimensional float vector (array of numbers) representing the semantic content of the input text, suitable for similarity search, clustering, or RAG retrieval pipelines.","failureModes":["Text exceeds 32,000 character limit — request rejected","Empty or missing text field — validation error","x402 payment failure (insufficient funds or wallet issue) — payment error returned","OpenAI upstream outage — 502/503 returned","Malformed JSON request body — 400 bad request"],"whenToPreferThis":"Choose this endpoint when you need a high-quality 1536-dim embedding from OpenAI's text-embedding-3-small model without managing your own OpenAI API key, and you want to pay per call via x402 micropayment. Ideal for agents building RAG pipelines, semantic search, or clustering on the fly without provisioning credentials.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T19:14:05.146Z","isFirstParty":false}