Server has 10 tools with consistent naming (verb_noun pattern: get_*, suggest_*, summarize_*) and descriptions present for all. However, descriptions are generic and lack LLM-optimization guidance. Input schemas are visible and typed, but parameter descriptions are minimal (avg ~30 chars vs baseline 72 chars). No output schemas documented. Error handling returns strings but lacks recovery guidance or error classification. Tools 6-10 (LLM-backed) lack explicit error categorization. No tool annotations (readOnlyHint, etc.). Composition is sound, each tool has one responsibility, but descriptions don't explain WHEN to use each tool vs alternatives (e.g., suggest_clothing vs summarize_weather).

• Expand parameter descriptions to 50-100 chars. E.g., 'city: Name of the city (e.g., London, New York, Tokyo). Accepts common names and abbreviations. Returns error if city not found in weather database.'
• Document output schemas as JSON objects with typed fields. E.g., get_current_weather returns {temperature_c: number, feels_like_c: number, humidity_percent: number, wind_speed_ms: number, condition: string, city: string, country: string}.
• Add error classification to descriptions: 'Returns user-fixable error if city not found (suggest search_cities tool). Returns retryable error if API temporarily unavailable.'
• Add tool annotations to schema: set readOnlyHint=true for all tools (they do not modify state). This signals to agents that these calls are safe to retry.
• Differentiate LLM-backed tools in descriptions. E.g., suggest_clothing: 'AI-generated recommendation based on current weather. Use for personalized advice. For raw data, use get_current_weather.' This prevents tool confusion.

Inferred effective spec: <=2025-11-25.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry