Single tool 'execute_skill_action' has a reasonable schema with typed parameters and descriptions, but the tool definition itself is not explicitly registered in the MCP server code. The mcp_server.py only prints a JSON status message and does not implement the MCP protocol's tool registration, request handling, or response format. The tool exists only in documentation/client code, not in the actual server implementation. Schema is present but descriptions are generic (10-50 chars for tool, 30-80 chars for params). No error handling, no output schema documentation, no idempotency guarantees. The server does not handle tool invocation requests at all.

• Implement full MCP protocol in mcp_server.py: handle initialize requests, register tools with names/descriptions/schemas, and implement the tools/call request/response cycle per the MCP spec.
• Expand tool description to 100-150 chars explaining: (1) what it does (generates AI-optimized proposals), (2) when to use it (when you have deal context and competitor intel), (3) what it returns (proposal sections with win rate prediction).
• Document the output schema as a JSON Schema object with all fields typed: sections_generated (array of objects with section, word_count, personalization_score), predicted_win_rate_pct (number 0-100), win_rate_lift_vs_template_pct (number), key_differentiators_highlighted (array of strings).
• Add enum constraint to proposal_type parameter: ['enterprise_saas', 'mid_market_saas', 'smb_saas'] or similar. Add min/max to deal_size_usd (e.g., 10000-10000000). Expand param descriptions to 60-100 chars each with format hints.
• Add error handling documentation: specify which errors are retryable (e.g., timeout), which are user-fixable (e.g., invalid proposal_type), and provide recovery guidance (e.g., 'If proposal_type is invalid, use one of: enterprise_saas, mid_market_saas').