Single tool with minimal definition quality. Tool name 'getWeatherDetailsForCity' uses camelCase (non-standard verb_noun pattern) and lacks clarity on what 'details' means. Description is only 31 characters, below the 50-char minimum for LLM optimization. Input schema present but sparse: only 'city' parameter with basic string type, no format constraints, no enum, no min/max length. No output schema documented. Error handling returns a plain string message without structured guidance. Tool lacks idempotent/readonly annotations despite being read-only. No pagination, no result limits documented. Overall: bare-minimum implementation suitable only for trivial use cases.

• Rename tool to 'get_weather_forecast' (snake_case, clear verb_noun). Update description to 50-200 chars: 'Retrieve current weather forecast for a US city. Returns temperature, conditions, and 3-day forecast. Useful before planning outdoor activities or travel.'
• Add input validation: city parameter should specify '2-50 character US city name (e.g., Seattle, New York). Only US locations supported.' Add error recovery hint in description: 'If city not found, try alternative spelling or nearby major city.'
• Document output schema explicitly. Instead of returning formatted string, return structured JSON: {city, forecast: [{period, temperature, unit, shortForecast, detailedForecast}], retrievedAt}. Enables LLM to extract and reuse individual fields.
• Enhance error messages with recovery guidance. Current: 'Error: Could not retrieve weather for X. Note that this service only supports locations within the United States.' Better: 'City not found. This service supports US locations only. Try: (1) check spelling, (2) use major city name, (3) call list_supported_cities() for alternatives.'
• Add tool annotations: @Tool(name='get_weather_forecast', description='...', readOnlyHint=true, idempotentHint=true) to signal safety to agents and enable optimizations.