Critical failures across all dimensions. The single tool 'execute_skill_action' has a minimal description (67 chars, below baseline 194), but the core issue is that the MCP server implementation is non-functional. The mcp_server.py file does not implement the MCP protocol at all, it merely prints a JSON status message and exits. There is no request/response loop, no tool invocation handling, no schema validation, and no error handling. The tool definition exists only in documentation; the actual server cannot receive or process tool calls. Input schema is present (model_identifier, batch_size) but the server has no mechanism to invoke the underlying client.benchmark_inference() method. This is a stub implementation, not a working MCP server.

• Implement a proper MCP request/response loop in mcp_server.py. Use the official MCP Python SDK or implement JSON-RPC 2.0 message handling to receive CallTool requests and return CallToolResult responses.
• Expand tool description to 100-200 chars. Example: 'Benchmark LLM inference throughput on specified hardware. Returns tokens/sec, time-to-first-token, and peak VRAM. Use this to compare model performance across batch sizes and hardware configurations.'
• Add constraints to batch_size parameter. Example: 'Batch size for inference (1-256; typical values 1, 8, 16, 32). Larger batches increase throughput but require more VRAM.'
• Document the output schema in the tool definition. Declare that the response is a JSON object with fields: tokens_per_sec (number), time_to_first_token_ms (number), vram_peak_gb (number).
• Add error handling with recovery guidance. Catch exceptions from benchmark_inference() and return structured errors like: 'Model not found: gpt-5.6-luna. Available models: [list]. Try a different model_identifier.' or 'Batch size too large for available VRAM. Try batch_size <= 8.'