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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns scores grouped by benchmark and specifies reasoning_mode and agent_framework as secondary dimensions, which is useful behavioral context. However, it does not mention potential side effects, authentication requirements, rate limits, or whether results are historical or filtered. As a read-only 'get' operation, risk is low, but more explicit disclosure of limitations would improve transparency.

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?

Two sentences that front-load the core function and include a concrete use case. No redundant words or vague phrasing. Highly efficient and well-structured.

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 two-parameter tool with an output schema, the description covers the main purpose, the grouping of results, and the use case. It does not mention pagination or error conditions, but given the simplicity and the presence of an output schema, it is sufficiently complete for an agent to call correctly.

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 coverage is 100% and both parameters have meaningful descriptions in the schema. The description adds minimal value beyond the schema, only reinforcing that all scores are returned and that benchmark filtering is optional. It does not provide syntax or format details, so baseline 3 applies.

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: 'Get all benchmark scores for a model across eval sources.' It lists concrete benchmark examples and clearly distinguishes itself from siblings like get_model_trend or rank_models by focusing on benchmark scores. The purpose is unambiguous.

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?

Explicitly states a use case: 'Use to compare a model's strengths across tasks.' This provides clear context for when to use the tool, though it does not explicitly mention alternatives or exclusion conditions. The stated purpose is sufficient for an agent to select it appropriately.

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