list_models

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

With no annotations provided, the description carries the burden of disclosing behavior. It explains the return format: 'Dictionary with checkpoints, loras, default_checkpoint, and currently_loaded' and provides examples of content types. It also implies a read-only operation, though it does not explicitly state side-effect-free behavior.

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 well-structured with a clear opening, bullet list, usage tip, and return summary. It is slightly redundant between the first sentence and the 'Use this to discover' line, but overall it is efficiently composed.

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 no-parameter listing tool, the description covers the purpose, usage context, and output shape. An output schema exists, so the description does not need to detail return values further; it is complete within the scope of the tool.

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 the schema is fully covered (100%), so the description has no parameters to explain. The baseline for 0 params is 4, and the description adds no unnecessary parameter info.

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 'List available checkpoints and LoRAs for image generation', which is a specific verb+resource. It distinguishes the tool from siblings like load_checkpoint or generate_image by focusing on discovery rather than loading or generating.

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?

It gives explicit usage context: 'Use this to discover what models are available before generation.' This tells the agent when to invoke this tool (prior to generation) and implies it is complementary to loading/generating siblings, though it does not explicitly exclude alternatives.

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