PyWeatherMCP has basic tool definitions with descriptions and input schemas, but falls short of production quality. All 6 tools have descriptions (10-50 chars, below the 50-200 char baseline for LLM optimization) and visible input schemas with type declarations. However, parameter descriptions are minimal or absent, most params lack guidance on format, constraints, or valid ranges. Output schemas are not documented; tools return free-text strings rather than structured JSON. Error handling is generic ('Unable to fetch alerts') with no recovery guidance. No tool annotations (readOnlyHint, destructiveHint) despite clear risk profiles. The server uses file-based memory (weather_memory.json) for state, which is fragile and not production-grade. Tool composition is reasonable (6 focused tools), but descriptions are too terse to guide LLM selection effectively.

• Expand tool descriptions to 50-200 chars. Example: 'Get active weather alerts for a US state. Use this to check for severe weather warnings, tornado watches, and emergency alerts. Requires a two-letter state code (CA, NY, TX, etc.).'
• Replace example values with enum constraints. Declare state as an enum of valid US state codes rather than 'e.g. CA, NY'. This prevents LLM hallucination and is machine-parseable.
• Document output schemas as structured JSON. Instead of returning formatted strings, return objects like {alerts: [{event, area, severity, description}], state, count}. This enables LLM parsing and downstream tool chaining.
• Add actionable error messages with recovery hints. E.g., 'Unable to fetch forecast: coordinates out of US coverage area. Try a location within the continental US.' or 'API timeout (retryable). Try again in 5 seconds.'
• Add tool annotations. Mark save_favorite and clear_history with destructiveHint=true. Mark get_alerts, get_forecast, get_favorites, get_history with readOnlyHint=true. This guides agent planning.