Critical gaps across all dimensions. Tool names lack action verbs (list_tools, tell_joke, fortune are generic). Descriptions are minimal (10-50 chars) and lack context for LLM selection. No input schemas visible for any tool, parameters inferred from function signatures only. No output schemas documented. The one substantive tool (extra_order_from_content) has a Chinese description that LLMs may struggle with. No error handling guidance, no parameter validation hints, no security considerations. This server reads as a proof-of-concept, not production-ready.

• Add explicit JSON Schema input definitions for all tools via FastMCP's schema parameter. Example: mcp.tool(name='extra_order_from_content', description='...', input_schema={'type': 'object', 'properties': {'content': {'type': 'string', 'description': 'Message text containing order information'}}, 'required': ['content']}).
• Rename tools to start with clear action verbs: 'generate_joke' (not 'tell_joke'), 'draw_fortune' (not 'fortune'), 'list_available_tools' (not 'list_tools'). Verify names match the 54 Agentic Tool Patterns verb list.
• Expand all descriptions to 50-200 characters. Include WHAT the tool does, WHEN to use it, and what format it returns. Example: 'Generate a random joke in Chinese. Use when the user asks for humor or entertainment. Returns a single-line joke string.'
• Translate extra_order_from_content description to English and add parameter descriptions. Specify that 'content' should be a message string and that output is JSON with fields: order_id, product_name, quantity.
• Document output schemas for all tools. Specify return type (string, JSON object, array) and field names. Add error handling guidance: 'If order extraction fails, returns error message with suggestion to provide clearer order text.'

Inferred effective spec: <=2025-11-25.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry