Three tools with basic schemas and descriptions, but significant gaps in parameter documentation and output structure. All tools have descriptions (10-50 chars, below the 50-200 char baseline for LLM optimization). Input schemas present but parameters lack detailed descriptions beyond the bare minimum. No output schemas documented. Error handling is minimal (get_weather returns error strings, not structured recovery guidance). Tools are simple and well-named (verb_noun pattern), but lack the depth needed for production agent use.

• Expand tool descriptions to 50-200 characters. Example: 'add: Adds two numbers and returns the sum. Use for arithmetic calculations. Accepts floats; returns float.' This gives LLMs context for selection and output expectations.
• Document output schemas explicitly. For add/multiply, declare 'Returns: number (float)'. For get_weather, declare 'Returns: string (formatted weather summary with temp, description, humidity)' or return structured JSON {temp: number, description: string, humidity: number}.
• Enhance parameter descriptions with constraints. Change 'First number' to 'First number (float, any value)' and 'City name' to 'City name (string, e.g. London, Paris, New York, OpenWeatherMap format)'.
• Implement structured error responses. Instead of returning error strings, return JSON {success: false, error: 'city_not_found', message: 'City not found. Try a different spelling or use ISO country code.', retryable: true}. This guides LLM recovery.
• Add tool annotations (readOnlyHint, idempotentHint) to signal safety. All three tools are read-only and idempotent, declare this via tool metadata so agents know they can retry safely.