Two tools with basic verb-noun naming (get_alerts, get_forecast). Both have short descriptions (34-40 chars, below the 50-200 char baseline for LLM optimization). Input schemas are present with typed parameters and descriptions, but output schemas are completely undocumented, tools return plain strings with no structured format. No error handling guidance, no parameter constraints (e.g., state code validation), and no indication of what fields the LLM should expect in responses. Composition is reasonable (two distinct read-only operations), but lack of output documentation and minimal parameter validation significantly limit production readiness.

• Document output schemas for both tools. Specify that get_alerts returns a formatted string with event, area, severity, description, and instructions fields; get_forecast returns periods with name, temperature, wind, and forecast text. Consider returning structured JSON instead of plain text.
• Expand tool descriptions to 80-150 characters. Example: 'Get active weather alerts for a US state. Returns event type, affected area, severity level, and recommended actions. Use when user asks about weather warnings or hazards.'
• Add enum constraint for 'state' parameter: restrict to valid 2-letter US state codes (AL, AK, AZ, ..., WY). Document in description: 'Two-letter US state code (must be valid state abbreviation)'.
• Add range constraints for latitude/longitude: 'Latitude must be between -90 and 90; longitude between -180 and 180. Use decimal degrees (e.g., 40.7128 for New York).'
• Implement structured error responses with recovery guidance. Example: 'Invalid state code: got "XX". Valid codes: AL, AK, AZ, ... Try search_states() to find the correct code.' Return error objects with type, message, and suggested_action fields.