Generate Color System

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnly/idempotent/destructive annotations, the description discloses deterministic behavior, WCAG 2.2 checking with auto-adjustment, and that it returns a real generated palette rather than advice. This gives the agent strong expectations about side-effect-free but substantive output.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Every sentence earns its place: the first defines scope and output, the second explains the accessibility guarantee, and the third conveys determinism and downstream steps. The description is dense but not bloated.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description carries the burden of explaining return value composition, and it does so thoroughly: tonal scale, neutral ramp, light/dark semantic tokens, and WCAG verification. For a one-parameter deterministic generator, nothing essential is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the only parameter, brand_color, is already described as a hex string with examples. The description adds the 'ONE brand color' emphasis but little new parameter-level meaning, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action ('Turn ONE brand color into...') and names the concrete output components: tonal scale, neutral ramp, and semantic tokens. It clearly differentiates from siblings like generate_design_tokens and audit_accessibility by framing this as palette generation followed by those downstream tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear usage context: a single brand color in, a full system out, plus an explicit downstream workflow ('Feed the result into generate_design_tokens, then audit_accessibility'). It stops short of stating explicit when-not-to-use cases or alternatives like generate_type_scale or generate_elevation_system.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.