Critical failures across all dimensions. The single tool 'execute_skill_action' has a reasonable schema with typed parameters and descriptions, but the MCP server implementation is fundamentally broken: mcp_server.py does not implement the MCP protocol at all, it merely prints a JSON status message and exits. There is no request/response loop, no tool invocation handling, no error handling, and no actual integration with the client logic. The tool definition exists in isolation but cannot be invoked. The server cannot receive or process tool calls from an MCP client.

• Implement a proper MCP server that listens for JSON-RPC 2.0 requests over STDIO (or preferably HTTP/SSE), parses tool call requests, invokes execute_skill_action via the client logic, and returns structured responses.
• Document the output schema for execute_skill_action. Include field names, types, and descriptions for market_quadrant, white_space_opportunity, competitor_price_index, pricing_power_score, and brand_storytelling_angle.
• Expand the tool description to 80 - 150 characters explaining: (1) what analysis it performs, (2) when to call it (e.g., 'Use this to identify market positioning gaps and pricing strategy for e-commerce products'), (3) what it returns (positioning quadrant, competitor benchmarks, storytelling angle).
• Add input validation and error handling. Validate that category is one of a known set (office_wellness, wellness_wearable, etc.), price_usd is positive, and product_title is non-empty. Return actionable error messages if validation fails.
• Add parameter descriptions for category and price_usd. Currently, category lacks guidance on valid values; price_usd lacks range constraints. Specify: 'category: One of office_wellness, wellness_wearable, fitness_tracker, etc.' and 'price_usd: Product price in USD (must be > 0)'.