Six tools with complete input schemas and descriptions, but descriptions are minimal (10-20 chars) and lack LLM-optimized guidance. Parameter descriptions are present but generic. No output schemas documented. No error handling guidance. Tools follow verb_noun naming but lack context on when to use each vs. alternatives. No tool annotations (readOnlyHint). Composition is sound (each tool does one thing), but descriptions do not explain dependencies or prerequisites.

• Expand each tool description to 50 - 150 characters. Include WHAT it does, WHEN to use it (vs. similar tools), and what it returns. Example: 'Audit a palette for WCAG contrast compliance and color-vision deficiency simulation. Call this first to validate a palette before using it in solveCategorical or generating ramps.'
• Document output schemas for all tools. Specify the structure of returned color arrays, audit results, and contrast ratios. Example: 'Returns {colors: string[], warnings: string[], passesWCAG: boolean}'.
• Add format constraints to color parameters. State that colors must be 6-digit hex strings (e.g., '#FF0000' or 'FF0000'), and reject other formats with clear error messages.
• Add tool annotations to the schema. Mark all tools with readOnlyHint: true to signal to LLMs that they are safe to call without confirmation.
• Document parameter ranges and enums. For simulateColor, enumerate valid visionType values (normal, deuteranopia, protanopia, achromatopsia) as an enum constraint, not just in the description.