Critical failures across all dimensions. The single tool 'execute_skill_action' has a minimal description (56 chars, below the 10-1024 baseline), lacks output schema documentation entirely, and the input schema is present but sparse. The server code shows only a JSON print statement with no actual MCP protocol implementation, no tool registration, no request/response handling, no error handling. The client.py and example_usage.py files are disconnected from the MCP server and do not demonstrate integration. This is a stub implementation, not a functional MCP server.

• Implement the full MCP protocol in mcp_server.py: add a request/response loop that parses incoming tool calls, invokes the skill, and returns structured results with proper error handling.
• Document the output schema explicitly. Specify all fields returned by execute_skill_action (e.g., evaluation_run_id, task, benchmarks_evaluated, accuracy metrics, template_url) with types and descriptions.
• Expand the tool description to 100-200 characters explaining: (1) what prompt engineering taxonomy evaluation does, (2) when to call this tool (e.g., to optimize prompts for a task), (3) what the returned metrics mean (zero-shot vs. few-shot vs. directional stimulus accuracy).
• Add parameter constraints: document the expected range for evaluation_benchmarks_count (e.g., 1-10000), format for base_task_prompt (free text, max 2000 chars), and any prerequisites (e.g., 'task must be a clear, single-sentence prompt').
• Add error handling with recovery guidance: document what happens if the task prompt is malformed, if benchmarks_count exceeds limits, or if the evaluation service is unavailable. Return actionable error messages (e.g., 'Benchmarks count must be 1-10000; got 50000. Reduce and retry.').