This server has a single tool, execute_skill_action, that is advertised in mcp_server.py but has NO actual implementation visible in the provided source code. The tool name is vague (lacks clear verb-noun structure), there is no schema definition, no parameter documentation, and no error handling. The client.py and example_usage.py files show a separate AgentCostOptimizationOrchestratorClient class with an optimize_route method, but this is NOT registered as an MCP tool and does NOT match the advertised execute_skill_action interface. The mcp_server.py merely prints a JSON announcement that the tool exists but provides zero tool registration, schema, or implementation details. Per the hard scoring rules: if a tool has no input schema and no description visible in the source, both schema and description scores are 0. Since the tool is only inferred (advertised in output but not explicitly registered with a handler), the overall tool score is capped at 50. The average of one tool at 5-10 puts the definition quality score near the floor.

• Define execute_skill_action with a proper JSON Schema input object listing all parameters (e.g., prompt, max_budget_cents, model_name, etc.) with type, description, and constraints.
• Expand the tool description to 80-150 characters explaining: 'Execute a cost optimization skill action to select the optimal LLM model based on budget and context. Accepts a prompt and budget limit; returns routed model, estimated cost, and savings percentage.' Include when to use it (when you need cost-effective model routing).
• Rename the tool to reflect its actual purpose. Consider 'optimize_model_route', 'select_cost_efficient_model', or 'get_optimal_model_for_prompt', these are more specific and convey the action.
• Implement the tool handler in mcp_server.py. Replace the stub JSON output with actual MCP protocol calls (initialize, list_tools response with full schemas, and a tools/call handler that delegates to the AgentCostOptimizationOrchestratorClient.optimize_route() method).
• Add parameter documentation: describe prompt (string, min 1 char), max_budget_cents (float, 0.01 - 1000.0), and any other inputs. Add return schema documenting routed_model (string enum), estimated_cost_cents (float), savings_pct (float 0 - 100).
• Add error handling with recovery guidance. E.g., if the prompt is too short or budget is exceeded, return 'Cannot optimize: prompt is {length} chars but minimum is 10. Provide a longer prompt.' for the first scenario.
• Validate inputs before calling optimize_route(). Ensure prompt is not empty, max_budget_cents is positive, and handle edge cases (very large prompts, zero budget) with clear error messages.