Three tools with complete input schemas and descriptions. Naming follows verb_noun pattern (get_*, plan_*). Descriptions are adequate (50-100 chars) but lack actionable context for LLM selection. All parameters have type definitions and descriptions. No output schemas documented. Error handling exists but recovery guidance is minimal. Tool composition is sound (single responsibility), but parameter descriptions could specify constraints more explicitly (e.g., days range 1-16 is in description but not as JSON Schema minumum/maximum).

• Add outputSchema to all three tools documenting the response structure (e.g., {type: 'object', properties: {temperature: {type: 'number'}, humidity: {type: 'number'}, ...}}). This enables LLMs to plan downstream operations.
• Convert natural-language constraints to JSON Schema: add minimum: 1, maximum: 16 to days parameter in get_forecast; add pattern or enum to country parameter if only ISO-3166 codes are valid.
• Expand tool descriptions to 100-150 chars, adding WHEN-to-use context: 'Get current weather for a city to check conditions before suggesting outfits. Use get_forecast for multi-day planning.'
• For plan_outfit, either add units and activity as input parameters (with enum constraints) or clarify in description that elicitation happens via multi-turn interaction (MRTR pattern).
• Add error recovery hints to descriptions: 'If city not found, try with country code (e.g., São Paulo, BR) or call a city search tool first.'