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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds no additional behavioral context such as ranking behavior, result format, or limitations, but does not contradict the annotations either.

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?

A single, front-loaded sentence that communicates the tool's purpose and primary inputs with no wasted words. Every element is meaningful and 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?

Given the presence of a complete output schema and informative annotations, the description covers the essential decision context. It could mention that audience and brandAdjectives are optional, or explain how 'best' is determined, but the combination of schema and description is sufficient for most selection use cases.

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%, so parameters are already well-documented. The description maps 'product type', 'audience', and 'brand direction' to appType, audience, and brandAdjectives, but adds no extra semantics beyond what the schema already provides.

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 ('Recommend'), a clear resource ('MantleKit theme'), and the key inputs ('product type, audience, and brand direction'). This clearly distinguishes it from sibling tools like compare_build_vs_custom or generate_project_blueprint, which address different decisions.

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 a clear context: use this tool when you need a theme recommendation based on product type, audience, and brand direction. It does not explicitly list exclusions or alternatives, but the use case is sufficiently clear for a recommendation tool of this scope.

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