Critical failures across all dimensions. The single tool 'execute_skill_action' has a reasonable name and basic schema structure, but the implementation is fundamentally broken. The mcp_server.py file does NOT implement the MCP protocol, it merely prints a JSON status message and exits. There is no actual tool registration, no request/response handling, no parameter validation, and no error handling. The tool definition exists only in the rubric description, not in executable code. The client.py file is a standalone Python class unconnected to the MCP server. This is not a functional MCP server.

• Implement a complete MCP server that handles incoming requests, parses tool calls, validates parameters, and returns structured responses. Use the official MCP Python SDK or a reference implementation.
• Register tools via a tools/list endpoint that returns tool names, descriptions, and full JSON Schema input definitions. Include output schema documentation.
• Expand tool description to 150-250 characters explaining: what the tool does, when to use it (e.g., 'Use this to generate multiple export formats from a single master asset'), and what it returns (e.g., 'Returns URLs to exported PNG files and export duration').
• Add enum constraints for target_aspect_ratios (e.g., ['1:1', '9:16', '16:9', '4:5', '21:9']) and document the source_master_asset format (file path, URL, or asset ID with examples).
• Document the complete output schema: { exported_assets_urls: string[], total_files_generated: number, export_duration_sec: number, status: 'success'|'partial'|'failed' }. Include error responses with actionable recovery guidance.