list_top_models

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context about the output format ('compact Markdown table ~250 tokens') and the ranking focus, but doesn't cover other behavioral aspects like rate limits, data freshness, or error handling. The description doesn't contradict any annotations, but could be more comprehensive given the lack of structured 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 extremely concise (two sentences) and front-loaded with the core purpose. Every sentence earns its place: the first defines the action and categories, the second specifies the output format. No wasted words or redundant information.

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 the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is reasonably complete. It covers the purpose, categories, and output format, though it could benefit from more behavioral context. Without an output schema, the description helpfully specifies the return format, but additional details about ranking criteria or data sources would improve completeness.

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 100%, so the schema already documents all parameters thoroughly. The description adds marginal value by listing the category options and implying the ranking logic, but doesn't provide additional semantic context beyond what's in the schema. With high schema coverage, the baseline score of 3 is appropriate.

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 specific action ('List top-ranked LLM/VLM models'), the resource ('models'), and the scope ('for a category'). It distinguishes from sibling tools like 'compare_models', 'get_model_info', and 'recommend_model' by focusing on ranking and tabular output rather than comparison, detailed info, or personalized recommendations.

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 implies usage context by listing categories and mentioning the output format, but it doesn't explicitly state when to use this tool versus alternatives like 'compare_models' or 'recommend_model'. No exclusions or prerequisites are provided, leaving the agent to infer appropriate usage scenarios.

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