describe_ontology

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

No annotations are provided, so the description must convey behavioral traits. It describes an explanatory function but does not explicitly state it is read-only, non-destructive, or has no side effects. Given the tool's nature, this is acceptable but minimal.

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?

Two sentences, front-loaded with the main purpose. Every word contributes meaning. No fluff or repetition.

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?

With zero parameters and no output schema, the description adequately explains the tool's purpose. However, it could specify the output format (e.g., returns a textual explanation) to be fully complete.

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 tool has zero parameters and schema coverage is 100% (trivially). Per rules, baseline is 4. The description does not add parameter-level info, but none is 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 clearly states the tool explains the semantic token naming model, listing specific components (property classes, intents, contexts, states, modifiers, formula) and ends with a clear usage directive. It distinguishes itself from sibling tools like list_dimensions or list_brands by focusing on the naming model.

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 includes 'Use this to understand how semantic tokens should be structured,' providing clear context for when to use it. However, it does not explicitly mention when not to use it or compare to alternatives among the 30+ sibling tools, slightly reducing guidance.

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