Single tool 'execute_skill_action' has a basic description and minimal input schema. The tool name lacks a clear action verb (execute_skill_action is generic). Input schema defines two parameters with types and descriptions, but output schema is completely undocumented, the client.py shows the tool returns a complex dict with 9 fields, but this is not declared in the MCP server definition. No error handling, no parameter constraints (e.g., factory_cost_usd should have min/max bounds), and no guidance on when/why to call this tool. The mcp_server.py does not actually implement tool invocation, it only prints a static JSON response and does not handle incoming tool calls. This is a non-functional MCP server stub.

• Rename tool to 'calculate_transparent_pricing' or 'analyze_supply_chain_breakdown' to clearly signal the action and match the verb_noun pattern.
• Document the complete output schema in the MCP tool definition: declare all 9 response fields with types (string, number, boolean) and descriptions. Example: 'm2c_fair_retail_price_usd' (number): 'The fair retail price calculated by M2C markup (1.78x factory cost).'
• Add numeric constraints to 'factory_cost_usd': min=0.01, max=100000, description='Factory production cost in USD (must be positive, typically 1 - 50000).'
• Implement actual MCP protocol handling in mcp_server.py: parse incoming tool call requests, invoke the client logic, and return structured responses with proper error handling.
• Add input validation: reject negative factory_cost_usd, empty item_name; return actionable error messages like 'Invalid factory_cost_usd: must be > 0. Got: -5.0' to enable LLM self-correction.