Critical gaps across all dimensions. Tool descriptions are minimal (10-30 chars, well below 50-200 char baseline for LLM optimization). No visible input schemas in provided source code, cannot verify parameter types, constraints, or descriptions. Tool names follow verb_noun pattern (positive), but descriptions lack WHAT/WHEN/WHY context. No evidence of error handling, output schema documentation, or composition guidance. Korean-language descriptions further reduce LLM utility in English-first agent contexts. This server is not production-ready for agentic use.

• Add English-language descriptions (50-200 chars) to all tools. Example: 'search_hotels' → 'Search for available hotels by destination, check-in/check-out dates, and guest count. Returns hotel name, price, rating, and booking URL. Call this first to explore accommodation options.'
• Define and document input schemas for all tools. Specify parameter types (string, integer, enum), constraints (min/max, regex patterns), and descriptions. Example: create_activity should document activity_name (string, 1-200 chars), date (ISO 8601), duration_hours (integer, 1-24), etc.
• Document output schemas. Specify which fields are returned (e.g., activity_id, activity_name, date, duration_hours, status). Include IDs needed for chaining (e.g., activity_id for update_activity, delete_activity).
• Add pagination to list_* tools: accept limit (1-100, default 20) and offset/cursor parameters. Return total_count and next_cursor. Cap results at 50 items per call.
• Implement confirmation pattern for destructive tools: add optional dry_run=true parameter to delete_* tools. Return 'Would delete X records' without executing. Require explicit confirmation for actual deletion.

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

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