benchmark_get_model_stats

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

No annotations are provided, so the description carries the behavioral disclosure burden. It communicates a read-only intent through 'Get' and enumerates the returned information categories, but it does not describe aggregation details, data freshness, or error/edge-case 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?

One concise, front-loaded sentence with no filler or redundancy. Every word adds information relevant to selecting or invoking the tool.

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 one-parameter, no-output-schema tool, the description provides essential context: what is returned and the target model. Some detail about the exact response shape is missing, but the tool's simplicity makes the description reasonably complete.

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 already documents model_id with 100% coverage, so the baseline of 3 applies. The description does not add new parameter details, but 'for a specific model' aligns naturally with the single required parameter.

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 that this tool gets performance metrics, win rates, and ranking score for a specific model. It differentiates from leaderboard/comparison tools by scoping to a single model, though it does not explicitly name siblings.

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 intended use is implied: inspect statistics for one model. However, there is no explicit guidance on when to prefer this over benchmark_compare_models, benchmark_get_leaderboard, or benchmark_get_results, and no exclusions or prerequisites are given.

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