{"uid":"cap_lopTVlbDvc3rMgApYQgck","slug":"fp16-mixed-precision-mcp-6f0d9b59","name":"FP16 Mixed Precision MCP","description":"The premier global index of 1,069 monetized MCP nodes across 205 specialized subdomains. Gasless USDC runtime settlements via x402 V2 Spec on Base L2. Save 95% token context.","url":"https://api.m2mcent.com/fp16-mixed-precision-mcp/api/process?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":null,"responseSchema":{"type":"json","example":{"success":true}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.3","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":"$0.3/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.3","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.3","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_Hgaldf-5YrwSi5kx75XfW","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.3","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Processes data using FP16 mixed-precision computation, applying half-precision floating-point arithmetic optimized for performance-sensitive AI/ML workflows.","exampleAgentPrompt":"Can you run my numerical data through the FP16 mixed-precision processor? Here's the data: [0.123, 0.456, 0.789, 1.234, 5.678].","exampleUseCases":[{"title":"ML inference pipeline acceleration","prompt":"I have a batch of model activation values I need processed with fp16 mixed precision to reduce memory overhead and speed up inference — can you pass this tensor data through the fp16 processor?"},{"title":"Numerical data half-precision conversion","prompt":"Take this array of floating point numbers and process them through the fp16 mixed precision engine so I get back the half-precision representation: '3.14159, 2.71828, 1.61803, 0.57721'."},{"title":"AI model parameter optimization","prompt":"I want to apply fp16 mixed precision computation to these model weight values to see the precision tradeoff — can you run this data string through the mixed precision processor for me?"}],"resultDescription":"Returns the result of FP16 mixed-precision computation applied to the submitted data, including processed output values in half-precision floating-point format suitable for ML inference or numerical analysis pipelines.","failureModes":["Invalid or malformed data string returns 400 error","Data values outside representable FP16 range may produce infinity or NaN outputs","Payment failure via x402 protocol blocks request","Overly large data payloads may be rejected or time out","Non-numeric string data may produce unexpected transformation results"],"whenToPreferThis":"Choose this endpoint when you need to apply FP16 mixed-precision arithmetic to data for ML inference optimization, memory reduction, or numerical computing tasks. Prefer this over full FP32 processing when speed and memory efficiency are priorities and slight precision loss is acceptable.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T06:55:21.279Z","isFirstParty":false,"canonicalSlug":"fp16-mixed-precision-mcp-6f0d9b59"}