This server has critical definition gaps. The single tool 'execute_skill_action' lacks a visible input schema in mcp_server.py, only a hardcoded tool name is printed. No parameter definitions, types, or constraints are present in the code. The description is generic (28 chars) and does not explain what parameters the tool accepts, what it returns, or when to use it. The client.py file shows a process_audio() method with parameters (audio_uri, enhancement_modes) but these are NOT exposed as tool parameters in the MCP server definition. The tool appears to be a stub that prints a JSON status message rather than a functional MCP tool handler. No error handling, no output schema documentation, and no evidence of proper tool registration with input/output schemas.

• Define a proper JSON Schema for execute_skill_action input parameters. Include audio_uri (string, required), enhancement_modes (array of enums: 'dereverb', 'noise_removal', 'speaker_isolation'), and any other parameters the tool accepts. Add type and description to each parameter.
• Rename the tool to a more specific verb_noun pattern, e.g., 'separate_audio_stems' or 'enhance_and_separate_audio'. This helps LLMs understand the tool's purpose without reading the description.
• Expand the tool description to 100-200 characters. Explain: (1) What the tool does (separates audio into stems and applies enhancements), (2) When to use it (when you have noisy audio that needs cleaning), (3) What it returns (extracted stems with SNR improvements and enhancement metrics).
• Document the output schema with all fields: input_audio (string), enhancements_applied (array), extracted_stems (array of objects with stem_id, type, snr_improvement_db, file), reverb_reduction_pct (number), background_noise_reduction_db (number), output_studio_quality (boolean).
• Add error handling guidance. Document what happens if audio_uri is invalid, if enhancement_modes contains unknown values, or if the audio file is inaccessible. Provide actionable recovery steps for each error class.