This is a practice/learning project with 3 tools that have minimal documentation and no visible input validation. Tool names follow the verb_noun pattern (getWeatherByDate, addWeatherData, getAvailableDates), which is positive, but descriptions are extremely brief (10-32 characters), falling far below the 50-200 char baseline for LLM optimization. Input schemas are present with basic type information, but parameter descriptions are missing critical context (e.g., no explanation of date format requirements, humidity range, temperature units). No output schema documentation is visible. Error handling, retry guidance, and recovery patterns are absent from the code. The WeatherService implementation is not provided, so we cannot verify that parameters are actually validated or that errors are actionable.

• Expand getWeatherByDate description to ~100 chars: 'Retrieve weather information (status, temperature, humidity) for a specific date. Returns null if no data exists for that date. Call getAvailableDates first to find valid dates.'
• Expand addWeatherData description to ~120 chars: 'Create or update weather data for a date. Status must be one of: Sunny, Rainy, Cloudy, Snowy. Temperature in Celsius (-50 to 60). Humidity as percentage (0-100). Returns the updated weather record with timestamp.'
• Expand getAvailableDates description to ~80 chars: 'List all dates with recorded weather data. Returns an array of ISO 8601 date strings sorted chronologically.'
• Add explicit output schema documentation for each tool, e.g.: 'getWeatherByDate returns {date: string, status: string, temperature: number, humidity: number, recordedAt: string (ISO 8601)}'
• Add parameter-level constraints: 'date must be in YYYY-MM-DD format and refer to a valid date (past or future as applicable).'
• Implement error handling: return structured error responses with actionable guidance, e.g., 'Invalid date format. Expected YYYY-MM-DD (e.g., 2024-12-25).' instead of raw exceptions.
• For addWeatherData, document which fields trigger validation errors and how to correct them.
• Document whether addWeatherData is idempotent (calling twice with the same inputs produces the same result) or destructive (overwrites), so LLMs know if they can safely retry.
• Consider adding a batch variant: addWeatherDataBatch(List<WeatherEntry>) to reduce token cost if agents need to insert multiple days at once.