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

The annotations already mark this as read-only and idempotent, so no destructive warnings are needed. The description adds useful behavior al detail: result structure varies by source, source are combined when omitted, and task_type narrows model suitability. It leaves details like rate limits and pagination unspecified, but those are not central for this read-only list 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?

Three sentences: purpose, source/result mapping, and filtering behavior. Each sentence contributes a meaningful chunk of guidance, and the highest-level distinguishing detail is front-loaded.

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?

The description handles the two main sources, the all-source fallback, and task_type filtering, which covers the core flow. The only meaningful gap is that the third source, openrouter, is in the schema but not given the same result-shape explanation; a fuller sentence about what source=openrouter returns would make the description 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 description adds meaning beyond the schema by explaining that source selects both the dataset and the result shape, and that task_type maps to index/category semantics. The input schema's nested param descriptions handle details like arena, category, search_engine, and include_run_config; the tool description still adds the high-level relationship between source, return shape, and task_type.

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 opens with a specific verb-resource pair: "Compare model quality beyond price using third-party benchmarks." It names the concrete sources and the shapes they return, which distinguishes it from sibling tools like list-models and the ranking lists.

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?

It clearly states when to supply source, what each source returns, and the impact of omitting source. It also explains how task_type filters. It does not explicitly contrast against sibling benchmark/ranking tools, but for this tool's own options the guidance is actionable.

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