Both tools have minimal descriptions (under 50 chars) and lack parameter descriptions entirely. text_to_speech has no input schema visible in the decorator, only inferred from function signature. play_audio similarly lacks explicit schema. No output schemas documented. Error handling is minimal (only file-not-found check in play_audio). No security considerations for API key injection or command injection in play_audio. Naming is acceptable (verb_noun pattern), but overall quality is well below production baseline.

• Add explicit JSON Schema to both tools with type='string' and detailed descriptions for each parameter. E.g., text: 'The text to convert to speech (max 5000 chars, UTF-8 encoded)'; path: 'Absolute or relative file path to the audio file (must exist and be readable)'.
• Expand tool descriptions to 100-200 chars. text_to_speech: 'Converts text to speech using Deepgram API and saves the audio file to ~/Desktop/deepgram/audio/. Returns the file path. Note: This tool consumes Deepgram credits and is rate-limited to 10 calls/minute.' play_audio: 'Plays an audio file locally using macOS afplay command. Requires the file to exist and be in a supported format (MP3, WAV, AIFF). Returns success or error message.'
• Replace os.system() with subprocess.run() and proper path validation. Use shlex.quote() or pathlib.Path to prevent command injection: subprocess.run(['afplay', str(path)], check=True).
• Document output schema for both tools. E.g., text_to_speech returns {type: 'text', text: 'Success. File saved as: /path/to/file.mp3.'}, make this explicit so LLMs know the response format.
• Add error handling with recovery guidance. E.g., if Deepgram API fails, return 'Deepgram API error: {error_message}. Check DEEPGRAM_API_KEY is set and account has credits.' For play_audio, validate path exists and is readable before calling afplay.