Loaded Models

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

No annotations are provided, so the description must convey behavior. It states the tool lists models, which implies a read-only operation, but it does not explicitly confirm that no modifications occur. The inclusion of the GET endpoint hints at its non-destructive nature, but without explicit assurance, the transparency is moderate.

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 conveys all necessary information without any redundancy. It is well-structured and directly to the point, making it easy for an agent to quickly understand the tool's 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?

Given there is no output schema, the description successfully indicates what the tool returns: loaded models categorized by type, along with device and size. It provides enough context for an agent to know what to expect from the response, making the tool complete for its intended use.

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 input schema is empty, so there are no parameters to describe. According to the rubric, with 100% schema coverage (since no parameters exist), the baseline score is 3. The description does not need to add parameter details because there are none, and it does not introduce any unnecessary 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 tool's function: listing all loaded models by category, with device and size. It specifies the action (List), the resource (loaded models), and the additional details included, making the purpose unambiguous. This clearly distinguishes it from sibling tools that list only specific model types like checkpoints or LoRAs.

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 does not provide guidance on when to use this tool versus the many sibling list tools (e.g., list_checkpoints, list_loras). It lacks an explicit statement like 'Use this for an overview of all loaded models' or a comparison with alternatives. The only hint is the word 'all', but it does not directly address usage context.

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