This server is non-functional as an MCP implementation. The single tool 'execute_skill_action' has no input schema, no parameter descriptions, and a trivial 10-character description. The mcp_server.py file does not implement the MCP protocol, it merely prints a JSON status message and exits. There is no request/response handling, no tool invocation mechanism, and no actual integration with the GPU cluster client. The server cannot receive or process tool calls from an MCP client.

• Implement a proper MCP server with a request/response loop. Use the official MCP Python SDK (mcp package) or implement JSON-RPC 2.0 over STDIO with full protocol compliance.
• Define the execute_skill_action tool with a complete JSON Schema input specification. Include parameters for nodes_count, gpus_per_node, model_parameters_billions with types, descriptions, and constraints (e.g., nodes_count: integer, minimum 1, maximum 1024).
• Expand the tool description to 50-200 characters explaining WHAT it does, WHEN to use it, and WHAT it returns. Example: 'Optimize a decentralized GPU cluster for distributed training. Accepts node count, GPUs per node, and model size in billions of parameters. Returns cluster config, speedup metrics, and cost savings.'
• Document the output schema with all returned fields (cluster_config_id, total_gpus_orchestrated, model_param_size_b, flash_attention_3_fp8_kernel_speedup_pct, gradient_allreduce_overlap_efficiency_pct, network_topology, training_cost_savings_vs_hyperscalers_pct) with types and descriptions.
• Add error handling that guides recovery. E.g., if nodes_count exceeds available capacity, return 'Cluster capacity exceeded. Maximum 512 nodes. Try reducing nodes_count or splitting across multiple clusters.' instead of a generic failure.