benchmark

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

With no annotations provided, the description carries full burden and discloses key behavior: runs 5 specific challenges, catches hallucination/garbled output, and auto-selects models when no model_id is given. It lacks detail on return format (e.g., whether scores are per-challenge or aggregated) but is otherwise transparent.

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 lead sentence summarizing purpose, followed by a bulleted Args list. It is slightly verbose (e.g., the second paragraph adds context but could be integrated) but remains efficient for the tool's complexity.

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 complexity (5 challenge types, auto-selection logic) and the presence of an output schema (though not shown), the description covers the key points. It could mention the output structure more explicitly, but the context signals indicate output schema exists, reducing the burden.

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 has 0% description coverage, so the description compensates by explaining each parameter in the Args section (model_id, provider, min_tier, count). It adds meaning beyond the schema's type/default information, guiding the agent on usage.

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 states a specific verb+resource ('Quality-test models') and clearly distinguishes from siblings by detailing the 5 coding challenges (arithmetic, instruction following, etc.). It immediately tells the agent what the tool does and how it differs from other evaluation tools.

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 explains the default auto-selection behavior and optional parameters, implying when to use the tool (for quality testing). However, it does not explicitly state when to avoid using it or compare it to siblings like batch_judge or judge, leaving the agent to infer context.

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