Server has 5 tools with complete input schemas and descriptions present. Naming follows verb_noun convention (analyze_, get_, apply_, find_, style_). Descriptions are adequate (40-80 chars) but lack LLM-optimized detail on WHEN to use each tool and dependencies between them. Parameter descriptions exist but are minimal. Output schemas are not documented in the visible code. Error handling and recovery guidance are absent. No tool annotations (readOnlyHint/destructiveHint) despite clear risk profiles (WRITE vs READ_ONLY). Composition is sound, each tool has one responsibility and tools chain logically (analyze → get_profile → apply_style).

• Add output schema documentation for all 5 tools. Example: 'Returns: {profile: {sentence_length_avg: float, contraction_rate: float, ...}, status: string, timestamp: ISO8601}'. This enables LLMs to extract and chain results.
• Implement tool annotations via FastMCP's ToolAnnotations. Mark analyze_writing_style with destructiveHint=true; mark the 4 read-only tools with readOnlyHint=true. This signals to agents which operations are safe to retry.
• Expand descriptions to 100-150 chars with explicit WHEN-to-use guidance. E.g., apply_style: 'Compare a draft against the learned writing profile to identify style mismatches. Call get_style_profile first to ensure the profile is ready. Returns a rewrite brief with specific suggestions.'
• Add error handling with recovery hints. E.g., if corpus is empty, return 'No corpus files found. Add .txt, .pdf, or .docx files to the configured source directory and call analyze_writing_style again.'
• Document parameter constraints in descriptions. E.g., max_results: 'Maximum number of results to return (default 5, must be 1-20)' instead of relying on schema alone.

Inferred effective spec: <=2025-11-25.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry