Audio-analysis-mcp has 10 tools with reasonable naming (verb_noun pattern) and basic descriptions. However, critical gaps undermine quality: (1) Parameter descriptions are minimal or absent, most tools lack detail on expected formats, ranges, or constraints. (2) Output schemas are not documented in the source; LLMs cannot predict what fields to expect. (3) Error handling is not visible, no recovery guidance or actionable error messages. (4) No tool annotations (readOnlyHint/destructiveHint) despite clear risk distinctions (WRITE vs READ_ONLY). (5) Descriptions are terse (avg ~60 chars), below the 194-char baseline for A+ tools. Example: 'spectrum_analyze' has a 60-char description with no parameter detail on what 'audio_path' format is expected. The tool set is well-scoped (one concern per tool), but execution quality is below production baseline.

• Add output schema documentation to every tool. Example for spectrum_analyze: 'Returns {frequencies: [Hz], magnitudes: [dB], peaks: [{freq, mag}]}'. This enables agents to extract specific frequency bands.
• Expand parameter descriptions with format and constraints. Example: 'audio_path (string): Absolute or relative file path to .wav, .mp3, or .flac file in the workspace. Must exist; use import_audio to add files.'
• Add tool annotations via FastMCP. Mark destructive tools: @mcp.tool(annotations={'destructiveHint': True}). Mark read-only tools: @mcp.tool(annotations={'readOnlyHint': True}). This enables agents to reason about side effects.
• Include recovery hints in error scenarios. Example in import_audio: 'If file not found, call list_workspace_files() to see available audio files, or use import_audio to add a new file.'
• Expand tool descriptions to 100-200 chars. Example: 'Analyze frequency spectrum and spectral characteristics of audio. Returns frequency bins, magnitude peaks, and spectral centroid. Use this to identify dominant frequencies; use amplitude_analyze for envelope dynamics instead.'