kaggle_list_models

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

Annotations already declare readOnlyHint=true, so description adds value by mentioning 'Paginated' and 'Read-only', which clarifies output format and safety. No contradictions 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?

Single sentence that is front-loaded with key information: purpose, output format, pagination, and safety. No unnecessary words. Highly efficient.

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 5 parameters with 0% schema coverage and no parameter descriptions in the description, the tool's interface is underdocumented. However, the core functionality is clear. Adequate but incomplete.

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 description coverage is 0%, but description only mentions 'list/search' and 'paginated' without explaining any of the five parameters (page, owner, search, sort_by, page_size). Fails to compensate for lack of parameter documentation.

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?

Description clearly states 'List/search published models as a compact table', specifying the action and resource. It distinguishes from siblings like 'kaggle_get_model' which gets a single model, and 'kaggle_download_model' which downloads.

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?

Implies usage for listing and searching models but provides no explicit guidance on when to use instead of alternatives like 'kaggle_get_model' or 'kaggle_search_datasets'. No exclusions or when-not-to-use mentioned.

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