Single tool 'execute_skill_action' has a reasonable name starting with verb, but critical gaps undermine quality. Description is 156 chars (within baseline 34-392 range) but lacks WHEN to use it and prerequisites. Input schema visible with 3 parameters, all with descriptions and types (string), meeting basic requirements. However, no output schema is documented, LLMs cannot plan downstream calls or extract results. Parameter descriptions are minimal (10-30 chars) and lack format/constraint details. No error handling guidance. The tool definition is directly visible in mcp_server.py (tool registration via supported_tools list), so not capped at 50 for inference, but the implementation is skeletal, mcp_server.py only prints a JSON status message and does not actually handle tool invocation, request routing, or response marshaling. This is a stub, not a functional MCP server.

• Document the complete output schema for execute_skill_action. Include all returned fields (rewritten_text, clarity_delta_pct, processing_latency_ms) with types and descriptions so LLMs know what to expect.
• Expand parameter descriptions to 50-100 chars each. For target_tone, specify: 'Desired tone for rewrite. Valid values: CONCISE_PROFESSIONAL, FORMAL, CASUAL, TECHNICAL. Default: CONCISE_PROFESSIONAL.'
• Add WHEN/WHY guidance to tool description: 'Use this tool to improve clarity and professionalism of user-provided text. Call when user asks to rewrite, polish, or clarify highlighted content.'
• Implement proper MCP request/response handling in mcp_server.py. Parse incoming tool_call requests, route to client.py, and return structured responses with result and metadata.
• Add error handling guidance: 'If text is empty, return error: "highlighted_text cannot be empty". If tone is invalid, suggest valid options. If processing times out (>5s), return timeout error with retry guidance.'