VoxAvatar demonstrates solid tool design with clear verb-noun naming (play_animation, list_animations, control_window, get_status, show_message, set_character_state). All 6 tools have descriptions (avg ~150 chars, within baseline 34-392 range). Input schemas are present with proper types and enums. However, output schemas are not documented, LLMs cannot predict response structure for chaining. Parameter descriptions are adequate but lack format/constraint details (e.g., show_message.text maxLength is in schema but not emphasized in description). Error handling is minimal; no recovery guidance or actionable error messages visible. Tool composition is clean (single responsibility per tool), but missing pagination/batch variants and output schema documentation limits agent planning.

• Document output schemas for all tools. Example: list_animations should return {animations: [{name: string, description: string, trigger_scenario: string}], total: number}. This enables LLMs to plan downstream calls.
• Add recovery guidance to error scenarios. E.g., play_animation: 'If animation not found, call list_animations() to see available options.' This guides agent self-correction.
• Enhance show_message description to explain constraints: 'Text is sanitized to ~80 graphemes and displayed as plain text (no Markdown/links/images) to keep the UI clean and prevent token leakage into logs.'
• Link play_animation description to list_animations: 'Call list_animations() first to discover available actions and their trigger scenarios.'
• Clarify set_character_state ttl_ms in user terms: 'How long the state displays (e.g., 'speaking' for 3 seconds). Omit or 0 to use the state's default duration. Disconnecting clears the state immediately.'

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

• Relies on Sampling (deprecated) - integrate directly with the LLM provider API