This server has a single tool 'execute_skill_action' with virtually no implementation. The tool name is vague (execute_skill_action is generic and does not follow verb_noun pattern clearly). No input schema is visible in the source code, the mcp_server.py only prints a JSON status message with no actual tool registration, parameter definitions, or schema. The description is extremely brief (18 chars) and provides no actionable context for when or why an LLM should call this tool. No output schema is documented. The server appears to be a stub or proof-of-concept rather than a functional MCP implementation. The client.py and example_usage.py files show a separate client class unrelated to the MCP server protocol.

• Rename tool to reflect actual action: 'stream_screen_latent_diffusion' or 'generate_video_latent_diffusion' (verb_noun pattern). Match the client.py method name 'stream_screen_latent_generation'.
• Define complete input schema with typed parameters: capture_source (enum: DISPLAY_0_LIVE_FEED, WEBCAM_LIVE_FEED), target_fps (integer, 1-120), style_prompt (string, 1-1024 chars). Add descriptions for each parameter.
• Expand tool description to 100-200 chars: 'Stream live screen or webcam video through latent diffusion model with real-time style transfer. Returns WebRTC stream URL, latency metrics, and frame stability. Requires active display or camera source.'
• Document output schema with all fields: stream_session_id (string), source (string), fps (integer), latent_diffusion_latency_ms (integer), webrtc_stream_url (string), spatial_resolution (string), zero_drop_frame_rate_pct (number).
• Implement actual MCP protocol handler in mcp_server.py: parse incoming tool call requests, invoke the client method, return structured responses with proper error handling and recovery guidance.