This server has a single tool 'execute_skill_action' with virtually no definition. The tool name is generic and violates verb_noun conventions (should be 'process_return' or similar). No input schema is visible in the source code, only a hardcoded client mock that returns fixed data. The description is a single sentence with no guidance on parameters, expected inputs, or output structure. No parameter documentation exists. The implementation is a stub that prints JSON and exits; it does not actually implement MCP protocol message handling, tool registration, or request/response cycles. This is not a functional MCP server.

• Rename tool to a verb_noun pattern that reflects the actual action, e.g., 'process_return_request', 'approve_return', or 'generate_return_label'. This helps LLMs infer intent from the name alone.
• Define a complete JSON Schema for execute_skill_action inputs with types, descriptions, and enums. At minimum: order_id (string, required), return_reason (enum: wrong_size, size_too_small, defective, etc.), customer_tier (enum: standard, vip, premium). Include constraints in descriptions (e.g., 'order_id: 8-12 uppercase alphanumeric characters').
• Write a 50 - 200 character description that answers: What does this tool do? When should the LLM call it? Example: 'Process a post-purchase return request and generate a return authorization with instant exchange or store credit decision based on customer tier and return reason.'
• Document the output schema with all fields returned (order_id, return_authorization_id, decision, instant_store_credit_bonus_pct, return_shipping_label_url, package_protection_coverage, retention_revenue_saved_usd, return_portal_status). Include field types and descriptions.
• Implement the actual MCP protocol message loop in mcp_server.py: read JSON-RPC messages from stdin, parse tool calls, invoke the client logic, and write structured responses to stdout. Use an MCP library (e.g., mcp-python) or follow the MCP spec for STDIO transport.