Most design tokens are built for human use in tools like Figma or JSON files, making them hard for AI to interpret. When AI systems like Claude access these tokens through the Model Context Protocol (MCP), they see a jumble of nested objects without context. This often leads to incorrect code generation, where AI might use a token like "blue-5" instead of the intended "color-feedback-error." The problem lies in the lack of a semantic layer that provides clarity on the purpose and usage of each token. To make design tokens AI-readable, you need to add three key elements: semantic naming, descriptions, and relationships. Semantic naming conveys intent, turning generic names into specific usage guidelines. Descriptions clarify when to use a token, transforming a color value into actionable instruction. Relationships indicate how tokens connect with each other, helping AI understand dependencies. For example, knowing that "color-feedback-error" should be used alongside "color-feedback-errorText" helps the AI maintain consistency across components. Tokens are foundational to design systems. If they're ambiguous or lack intent, everything built on them inherits that confusion. By improving the clarity of your tokens, you enable AI tools to make informed decisions that cascade correctly through your design system. This approach is not just about immediate fixes but about creating a robust framework that enhances AI's ability to generate accurate design outputs. The article emphasizes that small changes in token documentation can yield significant improvements in AI readability and overall design consistency.