{"uid":"cap_d3wrMKc29ubFhWjcGdjlH","slug":"onnx-graph-pruner-mcp-0cbba049","name":"ONNX Graph Pruner 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/onnx-graph-pruner-mcp/api/process","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"payload":{"type":"string"}}},"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_eSsSLvCIYXmMVMZpnA3Mn","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":"Prunes and optimizes ONNX computational graphs by removing redundant nodes, unused operators, or dead branches to reduce model size and inference cost.","exampleAgentPrompt":"Can you prune this ONNX model graph to remove unused nodes and dead branches? Here's the serialized graph data: [graph_data].","exampleUseCases":[{"title":"Reduce inference model footprint","prompt":"I have an ONNX model that's too large for edge deployment — can you prune its graph to remove any dead nodes and unused operators so it runs faster on limited hardware?"},{"title":"Clean up exported ONNX from PyTorch","prompt":"I just exported a PyTorch model to ONNX and the graph has a lot of redundant nodes from the tracing process — can you prune it and give me back a cleaner, optimized version?"},{"title":"Optimize ONNX graph before quantization","prompt":"Before I quantize this ONNX model, I want to prune out any unused branches in the computational graph — can you process this graph data and return the stripped-down version?"}],"resultDescription":"Returns the pruned ONNX graph with redundant nodes, dead branches, and unused operators removed, resulting in a leaner computational graph suitable for faster inference or further optimization steps.","failureModes":["Invalid or malformed ONNX graph data returns an error","Empty or missing data field causes request rejection","Unsupported ONNX opset versions may fail silently or return unchanged graph","Very large graph data may time out","Non-ONNX formatted input data returns a parsing error"],"whenToPreferThis":"Choose this endpoint when you need automated pruning of ONNX computational graphs — especially after exporting from PyTorch or TensorFlow when the graph contains tracing artifacts, dead branches, or unused operators. Prefer this over manual graph editing when you want a programmatic, repeatable pruning step in an ML pipeline.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T18:58:01.243Z","isFirstParty":false}