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

Annotations already declare readOnlyHint=true, and the description adds valuable context: the perceptual model used, the output structure, and an example. It does not contradict annotations. While it doesn't disclose edge cases or limitations, it provides more behavioral context beyond what annotations alone offer.

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?

The description is only three sentences, with the main purpose front-loaded. The example sentence is illustrative and earns its place. No redundant or filler content.

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?

Given the tool has an output schema and read-only annotations, the description sufficiently covers the purpose, model, and example output. It does not explicitly list use-case alternatives, but for selecting this tool among many colour siblings, it provides enough context to make an informed choice.

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 description coverage is 100%, and the input schema already describes each parameter clearly. The description's example references two hex values but does not add additional semantic meaning beyond what the schema provides. Thus, baseline 3 is appropriate.

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 uses a specific verb 'Simulate' and resource 'mixing of two colours', and clearly distinguishes from RGB blending by mentioning CIE Lab subtractive model. It also specifies the output (mixed hex value and nearest archive match), making its purpose unmistakable and distinct from siblings like colour_compare or colour_harmonies.

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?

It clearly implies usage for subtractive pigment mixing by contrasting with RGB screen blending, and provides an example scenario. However, it does not explicitly name alternative tools or state when not to use it, so it falls slightly short of full explicit guidelines.

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