This server is non-functional as an MCP implementation. The mcp_server.py file contains only a print statement that outputs a JSON status message, it does not implement the MCP protocol at all. No tool registration, no request/response handling, no schema definitions. The single tool 'execute_skill_action' is mentioned in the status output but has no actual implementation, no input schema, no parameter descriptions, and no error handling. The client.py and example_usage.py files are standalone Python code unrelated to MCP. This is a stub, not a working MCP server.

• Implement the MCP protocol: add a request/response loop that parses JSON-RPC messages from stdin and writes responses to stdout. Reference the MCP spec (https://spec.modelcontextprotocol.io/) for message format.
• Define the input schema for execute_skill_action as a JSON Schema object with type, properties, required fields, and descriptions for each parameter (e.g., skill_name, model_config, input_data).
• Rewrite the tool description to be 50-200 characters and answer: What does this tool do? When should an LLM call it? What does it return? Example: 'Execute a speculative decoding inference job using a large verifier model and small draft model. Returns job ID, acceptance rate, and throughput metrics.'
• Add parameter descriptions for each input field. Example: 'target_large_model (string): HuggingFace model ID of the large verifier model (e.g., deepseek-ai/DeepSeek-V3). Required.'
• Document the output schema with field names, types, and descriptions. Example: {inference_job_id: string, large_verifier_model: string, small_draft_model: string, speculative_acceptance_rate_pct: number (0-100), token_throughput_per_sec: number, cost_reduction_vs_raw_serving_pct: number (0-100), json_grammar_constrained_decoding_ready: boolean}.