Three tools with minimal definitions. All have descriptions (10-30 chars, below the 50-200 char baseline for LLM optimization), no parameter descriptions beyond type hints, and no documented output schemas. Naming is verb-based (add, multiply, get_weather) but descriptions lack context on WHEN to use each tool or what they return. No error handling, no input validation guidance, no composition hints. Schemas are inferred from type hints rather than explicit JSON Schema declarations.

• Expand tool descriptions to 50-150 chars. Example: 'add: Adds two integers and returns their sum. Use for arithmetic calculations. Accepts any integer (positive, negative, zero).'
• Add parameter descriptions. Example: 'a: First integer operand (any value, including negative and zero)' and 'b: Second integer operand'.
• Document output schemas explicitly. Example: add returns {"result": integer, "operation": "add"}. get_weather returns {"location": string, "condition": string}.
• Add error handling. Example: get_weather should validate location is non-empty and return {"error": "Invalid location", "suggestion": "Try a city name like London or New York"}.
• Specify numeric constraints. Example: 'a and b must be integers in range [-2^31, 2^31-1]' or document any practical limits.