The ElevenLabs MCP server provides a single well-defined tool with comprehensive parameter documentation and proper schema structure. The tool name follows verb-first conventions (text_to_speech) and has a clear, detailed description. However, the tool lacks explicit error handling guidance, output schema documentation, and does not leverage all available parameter validation patterns. The server demonstrates above-average parameter documentation quality but misses opportunities for LLM-optimized descriptions and structured error recovery guidance.

• Add explicit output schema to tool description: 'Returns an object with {file_path: string, duration_ms: number, size_bytes: number, format: string}' or similar, matching what the underlying implementation actually returns.
• Document error scenarios and recovery paths: 'If voice_id not found, call list_voices() to discover valid IDs. If text exceeds 5000 characters, use handle_large_text(). If API rate limit hit, retry after 60 seconds.'
• Convert parameter constraints to JSON Schema: add 'enum' for model_id and output_format; add 'minimum' and 'maximum' for numeric ranges (stability, similarity_boost, style, speed).
• Formalize mutual exclusivity: use JSON Schema oneOf or add a validation note in the implementation that rejects calls with both voice_id and voice_name present.
• Add optional voice discovery tool or parameter: e.g., voice_description: 'e.g., female, british, professional' to allow natural-language voice selection without requiring opaque IDs.
• Implement cost preview or confirmation flow: add a 'dry_run' boolean parameter that returns estimated cost/duration without executing the API call, or add a separate preview_tts_cost() tool.
• Expand description with dependency hints: 'If you only have a voice name, use list_available_voices() first. If text is longer than 5000 chars, chunk it and call text_to_speech() multiple times.'