{"uid":"cap_LlcRp5_XLYcmCenDtwMIb","slug":"x402-agent-economy-lab-text-embed-endpoint-real-usdc-rail-da709ef2","name":"x402 Agent Economy Lab – Text Embed Endpoint (Real-USDC Rail)","description":"Machine-payable NLP micro-services: sentiment, entity extraction, summarization, report, batch. Paid per call in REAL USDC on Base mainnet.","url":"https://rqjzcd-ip-209-115-177-214.tunnelmole.net/v1/embed","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"text":{"type":"string"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.003","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":"$0.003/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.003","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.003","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_UGu9wytu8Lg0Ll2uS4VIs","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.003","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Accepts a text string and returns a vector embedding, payable per call in real USDC on Base mainnet via the x402 protocol.","exampleAgentPrompt":"Generate a vector embedding for this text: 'The quarterly revenue exceeded expectations due to strong product adoption.' — I need the embedding so I can store it in my vector database for semantic search.","exampleUseCases":[{"title":"RAG pipeline document ingestion","prompt":"Take this paragraph from our internal knowledge base and convert it into a vector embedding so I can store it in Pinecone for retrieval-augmented generation: 'Our return policy allows refunds within 30 days of purchase with a valid receipt.'"},{"title":"Semantic similarity search","prompt":"Embed this user query — 'best wireless headphones for commuting' — so I can do a cosine similarity search against our product catalog embeddings."},{"title":"Clustering customer feedback","prompt":"Convert this customer review into a vector embedding so I can cluster it with similar feedback: 'The onboarding was confusing but once I got the hang of it the product is great.'"}],"resultDescription":"Returns a dense numerical vector (embedding) representing the semantic content of the input text. The vector can be used for downstream tasks such as semantic search, similarity computation, clustering, classification, or storage in a vector database. Payment of $0.003 USDC is debited from the caller's wallet on Base mainnet per successful call.","failureModes":["Payment failure: insufficient USDC balance on Base mainnet returns a 402 Payment Required error","Empty or missing text field returns a 400 Bad Request","Text input exceeding model token limit may be truncated or rejected","Tunnelmole tunnel downtime causes connection refused or 502/503 errors","Network latency on Base mainnet payment settlement may cause timeout"],"whenToPreferThis":"Choose this endpoint when you need machine-payable, per-call text embeddings with micropayment settlement in real USDC on Base mainnet — ideal for AI agents operating autonomously in the x402 payment ecosystem. Prefer this over free or subscription-based embedding APIs when your agent stack requires trustless, crypto-native payment rails without API keys or billing accounts.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T00:39:47.198Z","isFirstParty":false}