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

The description adds meaningful behavioral context beyond the readOnlyHint annotation by detailing the returned content (axes, formula, labels, summary) and its source (baked case-file metadata). This helps the agent understand exactly what data to expect from a safe, read-only operation.

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 two sentences long, with the first sentence front-loading the most critical information (what is returned) and the second clarifying the data source. Every word earns its place, and there is no redundancy.

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 has no parameters, no output schema, and the description covers the key return components, it is largely complete. A minor gap is that it does not list the exact axis names, but this is acceptable for a summary description.

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?

With zero parameters, the baseline is 4. The description adds value by explaining the nature of the output, which compensates for the lack of parameter guidance.

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 tool returns the benchmark design, listing specific components like the 4 axes, OCS formula, decision labels, and scoring blocks. It distinguishes itself from sibling tools (get_case, get_results, compare_models, list_cases) by focusing solely on methodology rather than data or comparisons.

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?

While the description clarifies what the tool returns, it does not explicitly state when to use it versus alternatives (e.g., before analyzing results) or when not to use it. The context is clear but lacks comparative guidance.

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