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

The description discloses valuable behavioral context beyond the readOnlyHint: it explains that the tool is 'Backed by CIEDE2000 archive matching and Claude cultural intelligence' and lists what the output includes ('strengths, risks, avoid-if scenarios, and better alternatives'). This adds a methodological understanding and sets expectations without contradicting the read-only annotation.

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 concise and front-loaded: the first sentence states the core purpose, followed by the output verdicts, content included, underlying method, and relevant examples. Every sentence contributes value without redundancy, and the structure is easy to scan.

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?

For a tool with 5 parameters, an output schema, and a readOnlyHint, the description is remarkably complete. It covers purpose, output values, output contents, methodology, and practical examples, leaving no major gaps. The presence of an output schema means return values need not be described in detail, and the description does not over-explain.

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 schema already documents each parameter (hex, medium, markets, audience, use_case). The description's examples (e.g., 'luxury hotel brand in Japan') illustrate how parameters combine but do not add new semantic meaning beyond what the schema provides, so a baseline of 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 clearly states the tool's purpose: 'Evaluate a hex colour for a specific use case, market, and medium.' It specifies a decisive verb (Evaluate) and the resource (hex colour), and goes beyond by explicitly naming the three possible verdicts (use_with_confidence, use_with_caution, avoid), distinguishing it from sibling tools that assess other aspects like cultural risk or palettes.

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 conveys when to use the tool through its focus on evaluating a colour for a specific use case, market, and medium, and it provides illustrative examples. However, it does not explicitly mention when not to use it or compare it with alternatives like colour_cultural_risk or palette_verdict, so it lacks explicit exclusions but gives a clear context.

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