Two tools with basic verb-noun naming (get_alerts, get_forecast). Both have short descriptions (34-40 chars, below the 50-200 char LLM-optimized baseline). 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 concerns), but lack of structured output and error recovery patterns significantly limits agent usability.

• Expand tool descriptions to 80-150 chars. Example: 'Get active weather alerts for a US state (2-letter code). Returns event type, severity, area, and instructions. No alerts returns empty list.'
• Document output schema explicitly. Define what fields the string response contains (e.g., 'Returns formatted text with Event, Area, Severity, Description, Instructions per alert, separated by ---').
• Add enum constraint for 'state' parameter: restrict to valid 2-letter US state codes (AL, AK, AZ, ..., WY). Validate input and return 'Invalid state code: must be 2-letter US state abbreviation' on failure.
• Add range constraints for latitude (-90 to 90) and longitude (-180 to 180). Return actionable error: 'Invalid coordinates: latitude must be -90 to 90, longitude must be -180 to 180.'
• Return structured JSON objects instead of plain strings. E.g., get_alerts returns {alerts: [{event, area, severity, description, instructions}], count, timestamp}. This enables LLMs to extract and chain data reliably.