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MCP Server for AI model audio playback capabilities including text-to-speech, audio file playback, and voice management
This server has three tools with basic descriptions and partial schemas. All tools are explicitly defined in demo_audio_server.py with names, descriptions, and input parameters. However, critical gaps exist: (1) Descriptions are too brief (10-50 chars), well below the 194-char baseline for A+ tools. (2) Parameters lack detailed descriptions explaining constraints, ranges, and formats. (3) No output schemas documented, LLMs cannot predict what these tools return. (4) Error handling is minimal, no guidance on recovery or retryability. (5) Parameter 'rate' and 'volume' lack constraints (e.g., range for volume is stated as '0.0 to 1.0' but no minimum/maximum in schema). speak_text has the most complete definition but still falls short of production quality.
Get current audio playback status
List available text-to-speech voices for non-Chinese languages.
Play an audio file through the system audio
Convert text to speech and play it through the system audio. Supports multiple languages and voice selection (for non-Chinese text).
Stop current audio playback
Tool descriptions are too brief (10-50 characters vs. 194-char baseline). LLMs cannot determine when or why to select these tools. Missing context on prerequisites, side effects, and when to prefer one over another.
Parameters lack detailed descriptions. 'rate' parameter description is vague ('Speech rate in words per minute (optional, default varies by system)'), does not specify valid range (e.g., 50 - 300 wpm). 'volume' description mentions 0.0 - 1.0 but not in schema constraints. LLMs cannot validate inputs without explicit constraints in descriptions.
No output schemas documented. Callers (agents/LLMs) cannot predict what these tools return. speak_text returns {'success': bool, 'message': str, 'error': str?} but this is inferred from demo code, not formally declared. get_audio_status returns a 'status' object with fields like tts_available, pygame_available, music_playing, but structure is invisible in schema.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 10 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 35 | - | v1 |
Minimal error handling. Responses use generic 'success' boolean with optional 'error' string. No error classification (retryable vs. user-fixable). Errors like 'TTS unavailable' or 'audio device offline' are not actionable to an LLM, no guidance on whether to retry, skip, or ask user.
Parameters missing numeric constraints in schema. 'rate' (integer) has no minimum/maximum defined; 'volume' (number) has no constraints. Unbounded integers let LLMs pass absurd values (e.g., rate=999999, volume=1e10) that may break playback or cause timeouts.
Tool 'stop_audio' in ai_integration_demo.py not shown in schema registration (only visible in demo code). Unclear if it's registered with the MCP server. Tool definitions must be explicitly visible in the serving code, not just demo scripts.