This server is non-functional as an MCP implementation. The source code shows only a minimal stub (mcp_server.py contains a single print statement outputting JSON) with no actual MCP protocol implementation. Tool definitions are entirely absent, no schema registration, no parameter declarations, no tool registration mechanism. The single claimed tool 'execute_skill_action' exists only as a string in a JSON output, not as an actual callable tool with schema or description. Code references to 'client.py' and 'example_usage.py' are business logic unrelated to MCP; they do not register or expose tools via the MCP protocol. A real MCP server requires tool registration with input/output schemas, proper STDIO/HTTP transport handling, and protocol message handling. This implementation has none of that.

• Implement a real MCP protocol handler in mcp_server.py: add a STDIO message loop that reads JSON-RPC requests, parses them, routes them to tool handlers, and returns JSON-RPC responses. Reference the MCP spec (https://spec.modelcontextprotocol.io) for the exact message format.
• Register the tool with a proper schema. Define execute_skill_action with input JSON Schema including all parameters (e.g. moe_model_id, system_ram_gb, gpu_vram_gb), each with type, description, enum constraints (if applicable), and defaults.
• Rewrite the tool description to 50 - 200 characters, stating clearly: what the tool does, when to invoke it, and what it returns. Example: 'Execute MoE expert offload inference on specified model with given RAM/VRAM constraints. Returns active expert count, memory allocation, and throughput metrics.'
• Document input parameters with descriptions and constraints. For example: moe_model_id (string, model identifier like 'deepseek-ai/DeepSeek-V3'), system_ram_gb (integer, 1 - 1024, system RAM in GB), gpu_vram_gb (integer, 1 - 128, GPU VRAM in GB).
• Document the output schema with typed fields: offload_session_id (string), model (string), active_routed_experts_count (integer), cpu_ram_allocated_gb (number), pcie_prefetch_bandwidth_gb_sec (number), inference_tok_per_sec (number), vram_peak_usage_gb (number).
• Add error handling that returns actionable messages. For example: 'Invalid model_id. Supported models: deepseek-ai/DeepSeek-V3, deepseek-ai/DeepSeek-Coder-V2-236B' instead of a bare 500 error.