List AI Canvas categories

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

With annotations already declaring readOnlyHint, openWorldHint, and idempotentHint, the safety profile is covered. The description adds useful behavioral detail: it returns every category and includes component counts, which goes beyond what annotations provide. No contradiction with annotations.

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, with two sentences: the first states exactly what the tool returns, the second gives a practical usage example. No redundant or filler content.

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, parameterless tool with no output schema, the description sufficiently explains the return value (categories with counts) and provides usage context. It is complete enough for an AI agent to understand the tool's purpose and when to use it, especially given the rich annotations.

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, so there is nothing for the description to explain. Baseline of 4 is appropriate for a parameterless tool; the description correctly focuses on the return value and usage rather than parameter details.

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 ('Return') and resource ('every category in the AI Canvas standalone component library'), and specifies the output includes the number of components per category. This clearly distinguishes it from sibling tools like list_components or search_components, which operate on components rather than categories.

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 explicitly advises using this tool to 'orient before listing or searching,' which gives clear context and a specific use case. It does not name alternative sibling tools directly, but the guidance is unambiguous and helpful for an AI agent deciding when to invoke this tool.

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