list_label_axes

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

No annotations are provided, so the description carries the full burden. It clearly states it 'returns a list', which implies a read-only operation, and adds the specific detail of '24 axes'. However, it does not disclose potential errors, data formats, or any other behavioral nuances.

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 concise sentence that front-loads the action and resource. It contains no extraneous information and is highly efficient.

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 simple zero-parameter list tool with an output schema, the description is sufficiently complete. It clearly states what is returned and the count. The output schema covers return structure, so no further detail is needed.

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?

There are zero parameters, so the baseline is 4. The description adds context about the output (24 axes of the label vocabulary) which is helpful even though no parameter explanations are needed.

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 the specific verb '반환한다' (returns) and clearly identifies the resource: the 24 axes of the label vocabulary. This distinguishes it from sibling tools like list_labels (which likely lists labels) and describe_label (which describes a single label).

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 implies usage by stating exactly what the tool returns, but it does not explicitly mention alternatives or when to prefer this over list_labels or describe_label. The usage is clear from the name, but no direct guidance is provided.

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