Critical failures across all dimensions. The single tool 'execute_skill_action' has no visible input schema, no parameter descriptions, and a generic 18-character description that violates the 20-character minimum. The mcp_server.py file contains only a stub that prints JSON and does not implement MCP protocol message handling. No tool registration with proper schemas is visible. The client.py and example_usage.py files demonstrate domain logic but are not integrated into the MCP server. This is a non-functional MCP implementation.

• Add explicit JSON Schema to execute_skill_action with typed parameters (e.g., supermarket_branch: string enum, online_orders_count: integer with min/max). Reference the client.py dispatch_store_picking_wave signature.
• Expand tool description to 50 - 200 characters explaining WHAT it does, WHEN to use it, and what it returns. Example: 'Dispatch an optimized picking wave for supermarket omnichannel orders. Returns batch ID, efficiency metrics, and temperature-zone bins for curbside pickup.'
• Add descriptions to every parameter: supermarket_branch (enum of valid store codes), online_orders_count (integer 1 - 100, number of orders in wave).
• Implement the MCP protocol message loop in mcp_server.py: read JSON-RPC messages from stdin, dispatch tool calls, return structured responses with output schema matching client.py return fields.
• Rename tool to a clearer verb_noun pattern, e.g., 'dispatch_picking_wave' or 'create_picking_batch', to help LLMs disambiguate intent.