Server has 5 tools with basic schemas and descriptions, but significant gaps prevent production use. All tools have descriptions (10-30 chars, below the 50-200 char baseline for LLM optimization) and input schemas with type info. However, descriptions are generic and lack context about WHEN to use each tool or what happens on error. Parameter descriptions are minimal (e.g., 'First number' for 'a'). No output schemas documented. No error handling guidance. Database tools (list_users, find_user) return unstructured text instead of structured JSON. No tool annotations (readOnlyHint, destructiveHint). Naming is verb-noun compliant but descriptions fail to meet the 'WHAT, WHEN, WHY' standard required for LLM selection.

• Expand tool descriptions to 50-200 chars. Example: 'add_numbers: Adds two integers and returns the sum. Use this for arithmetic operations; use multiply_numbers for multiplication. Returns an integer result or an error if inputs are not valid integers.'
• Add parameter descriptions with constraints. Example: 'a: First integer operand (range: -2147483648 to 2147483647). b: Second integer operand (same range).'
• Return structured JSON from database tools instead of formatted strings. list_users should return [{id: 1, name: 'Alice', role: 'ML Engineer', skills: ['PyTorch', 'LangChain']}] so LLMs can extract fields reliably.
• Document output schemas for all tools. Example: 'Returns: {type: integer, description: The sum of a and b}' or 'Returns: {type: array, items: {type: object, properties: {id: integer, name: string, role: string, skills: array}}}'.
• Add error handling guidance. find_user should return structured error: {error: 'user_not_found', message: 'User Alice not found.', suggestion: 'Call list_users() to see available users.'} instead of a plain string.