This MCP server has critical structural issues preventing production use. The server claims to parse UI bounding boxes but provides only a stub implementation with hardcoded mock responses. The single tool 'execute_skill_action' has a schema (screenshot_width, screenshot_height, confidence_threshold) but the actual implementation in mcp_server.py does not register or validate this tool, it only prints a JSON status message. The client.py returns hardcoded mock data unrelated to input parameters. The tool is never actually invoked or wired to real processing logic. Parameter descriptions exist but are minimal. No error handling, no input validation, no output schema documentation, and the core MCP protocol handshake is incomplete (mcp_server.py does not respond to protocol requests beyond a one-time status print).

• Implement the full MCP protocol handshake in mcp_server.py: handle initialize request, return capabilities (including tools list), enter a request/response loop, and handle tool calls by dispatching to registered handlers.
• Register 'execute_skill_action' (or rename to 'parse_screen_ui') with a complete input schema: define types (integer, number) for all parameters, add min/max constraints (e.g., screenshot_width: min=640, max=4096; confidence_threshold: min=0.0, max=1.0), and mark required vs. optional fields.
• Add detailed descriptions to each parameter: 'screenshot_width (pixels): Resolution of the input image, typical range 640 - 4096. Affects detection granularity and API latency.' Include examples of valid values, not just types.
• Document the complete output schema with field types and descriptions: { 'screen_parse_id': string (unique parse run ID), 'interactive_elements_detected_count': integer, 'clickable_icons_count': integer, ... }. Explain what each field is used for downstream (e.g., 'Pass bounding_boxes_normalized_ast_url to a click simulator tool').
• Replace the hardcoded mock in client.py with real bounding box parsing logic. If this server wraps an external vision API (e.g., OmniParser), call it with the actual screenshot_width, screenshot_height, and confidence_threshold; do not ignore parameters.
• Add input validation before processing: check screenshot_width and screenshot_height are in valid ranges; validate confidence_threshold is 0.0 - 1.0; return a clear error if constraints are violated (e.g., 'Invalid confidence_threshold: 1.5, must be between 0.0 and 1.0').