models_list

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

No annotations provided, so description carries burden. Lists parameters but lacks behavioral details like authentication needs, rate limits, or return structure. The presence of an output schema reduces burden slightly, but still insufficient for full transparency.

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?

Extremely concise: a short paragraph with clear bullet-point style. Every sentence adds information without redundancy. Front-loaded with 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?

Coverage is good for a list tool with 4 parameters and an output schema. However, no guidance on pagination or filtering behavior beyond individual parameters, which could be expanded.

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?

Schema coverage is 0% (no schema descriptions), so description must compensate. Provides meaning for all 4 parameters, including sort_by options (hotness, downloadCount, etc.). Adds value beyond schema, though could be more detailed for page_size.

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?

Clearly states 'Search and list Kaggle models.' The verb 'list' and resource 'models' are specific, and it distinguishes from siblings like model_get or model_create which focus on single models or creation.

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?

Describes parameters but does not explicitly state when to use this tool instead of alternatives. Siblings like model_get for single model or datasets_list for datasets are not mentioned, leaving the agent to infer usage from context.

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