"author:JunHwan-Kwon" matching MCP servers:

Enables local static analysis of AI deployment artifacts, producing SHA-256 hashes, serialized structure, defects, cautions, evidence gaps, and limitations without executing model code.

Enables local static analysis of deployed AI model artifacts over MCP, exposing audit, diff, capabilities, and rule-explanation tools without uploading model bytes.

Enables local inspection of AI model deployment artifacts without executing model code, returning SHA-256, structure, defect analysis, and evidence-gap findings in Gemini CLI conversations.

Enables local candidate review of ONNX or TFLite models by checking static evidence and externally measured results against a fixed policy, producing accept/reject/hold evidence ZIPs for AI agent workflows.