Server defines 6 tools with schemas and descriptions present. All tools follow a consistent naming pattern (verb_noun) and have reasonable descriptions (80-150 chars). However, schemas lack output documentation, parameter descriptions are minimal, and there is no error handling guidance. No tool annotations are present. The server is functional but falls short of production-grade quality, descriptions are adequate but not optimized for LLM reasoning, and critical output schemas are undocumented.

• Add explicit output schemas to all tools. Document the structure of returned critique, analysis results, and contrast ratios. Example: 'Returns object with keys: overall_critique (string), composition (object), color_analysis (object), typography_analysis (object), accessibility_score (number 0-100), recommendations (array of strings)'.
• Expand parameter descriptions to 50-100 characters each. For image_data: 'Base64-encoded image data (PNG/JPEG/WebP, max 10MB). Omit the data: URI prefix.' For target_audience: 'Intended end-users (e.g., children, corporate professionals, accessibility-first). Helps frame critique context.'
• Document expected error modes and recovery paths in descriptions. Example for critique_design: 'Returns error if image is invalid or too large. If base64 is malformed, verify encoding. If API rate-limits, retry after 60 seconds.'
• Add tool annotations. Register all tools with { readOnlyHint: true } to signal they do not modify state. This clarifies safety properties to clients.
• Add regex patterns or enum constraints to check_color_contrast parameters. Specify that foreground and background must match '^#[0-9A-Fa-f]{6}$' or use a pattern constraint in the schema.
• For critique_design, clarify that brand_guidelines is optional and explain what happens if omitted. Example: 'If omitted, critique assumes no brand constraints. If provided, critique validates design against these colors/fonts.'
• Add success response examples in implementation comments (not tool description, examples in descriptions are anti-pattern). This guides developers on what output shape the LLM expects.