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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds the useful detail that results are segmented by grid size, but says nothing about aggregation, time window, or what happens for models without runs.

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?

A single front-loaded sentence with no filler; every word (metrics list, single-model scope, grid-size breakdown) carries 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?

An output schema exists, so return values need not be restated, and the parameter list is trivially small. The remaining gap is routing context: nothing tells the agent why to pick this over get_model_puzzles or compare_models.

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% for the single 'model' parameter, including a naming example ('gpt-5.4-xhigh'), so the schema does the heavy lifting. The description's phrase 'for one model' adds no format or constraint detail beyond that.

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?

States the specific resource (results for one model) and enumerates the metrics returned (accuracy, cost, latency, token use) with a scope qualifier ('broken down by grid size'). It does not explicitly distinguish itself from close siblings like get_model_puzzles or get_puzzle_results, so 4 rather than 5.

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 gives no when-to-use guidance, no prerequisites, and names no alternatives. With siblings such as compare_models, get_leaderboard, and get_model_puzzles in the same family, the agent gets no signal on when this tool is the right choice.

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