detect_variants

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

With no annotations, the description carries the full burden for behavioral disclosure. It implies a read-only detection operation but does not state whether it has side effects, what the output looks like, or any auth requirements. This is a significant gap for an unannotated tool.

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 a single sentence with no wasted words. It avoids repetition and is appropriately sized for a tool with one parameter and a straightforward purpose.

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?

The tool lacks an output schema and annotations, and the description omits what 'group them' means or what the return structure is. Even for a simple tool, the agent is left without essential context about the result format and behavioral expectations.

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?

The schema fully documents the single parameter 'figmaUrl' with a clear description ('Figma file URL (any format)'), so the description does not need to add parameter details. Baseline of 3 applies since schema coverage is 100% and the description offers no extra parameter information.

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 action ('Detect') and the target ('component variants'), plus the additional grouping behavior. It distinguishes itself from siblings like identify_components by focusing specifically on variants and grouping, though it could be more explicit about operating on a Figma file.

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?

No guidance is given on when to use this tool versus siblings like identify_components or get_node_details. The description only states what it does, offering no contextual hints or alternative recommendations.

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