Critical failures across all dimensions. The single tool 'execute_skill_action' has no visible input schema, no parameter descriptions, and a trivial 56-character description that violates the 10-1024 character guideline (too vague). The mcp_server.py file contains only a stub that prints JSON and exits, it does not implement the MCP protocol at all. No tool registration, no schema validation, no error handling. The 'client.py' and 'example_usage.py' files are standalone Python classes unrelated to MCP. This is not a functional MCP server.

• Implement a complete MCP protocol server with a request/response loop. Use the official MCP SDK (e.g., mcp-python) or follow the spec at https://spec.modelcontextprotocol.io/. The current mcp_server.py is a non-functional stub.
• Define the execute_skill_action tool with a complete JSON Schema input specification. Include all required and optional parameters with types (string, number, boolean, object, array), descriptions, enums, and constraints (min/max, pattern, length).
• Expand the tool description to 100-200 characters explaining: (1) what the tool does, (2) when to use it, (3) what it returns. Example: 'Routes a prompt to the optimal AI model based on token count and latency SLA. Returns selected endpoint, estimated cost, and savings percentage. Use when you need cost-optimized inference.'
• Document the output schema with all returned fields, their types, and descriptions. Include fields like selected_model_endpoint, estimated_cost_usd, cost_savings_pct, and any others the response contains.
• Add error handling that guides the LLM on recovery. E.g., if routing fails, return 'Unable to route: no models available for latency_limit_ms=<value>. Try increasing the SLA limit or check available models with list_models().' instead of a bare error code.