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

Annotations already declare readOnlyHint=true, openWorldHint=false and destructiveHint=false, so safety is covered. The description adds genuine context beyond that: the numbers come from held-out data, they are AMT's own authoritative values, and the agent must echo them verbatim rather than derive or guess them. That output-authority constraint is real behavioral guidance.

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, front-loaded with what the report contains and followed by the reporting constraint. Every clause carries information; the enumeration is a little packed but no sentence is wasted.

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?

With no output schema, the description correctly compensates by enumerating the report's fields, and a single fully documented read-only parameter needs no elaboration. Only the format or units of the numbers is left unspecified, a minor gap.

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?

There is a single parameter with 100% schema description coverage (model_id, sourced from list_models), so the schema already carries the meaning. The description adds nothing about the parameter, which is the expected baseline when schema coverage is complete.

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 resource and scope: the latest trained version's score on held-out examples, and enumerates the exact contents (accuracy, always-guess baseline, per-category accuracy, live status). This clearly separates it from siblings like list_models, mistakes, or data_quality without the agent needing to open any schema.

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 use case is strongly implied (fetch a model's evaluation report) and the description adds an operational directive for the returned numbers, but it never states when to pick this over alternatives or any prerequisites. Usage is inferable rather than explicit, so a 3 is warranted.

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