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

Annotations already establish read-only and closed-world behavior. The description adds useful context about model version and validation metrics, including a caveat about median F1 inflation, but it does not explain the returned score's scale, meaning, or interpretation.

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 a single front-loaded sentence, beginning with the resource and then placing model details in a parenthetical. The model-performance statistics are dense but relevant to interpreting the score.

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 simple read-only score tool, the schema and annotations cover invocation and safety, and model provenance adds some context. However, with no output schema, the description should explain what the score represents or its scale, and it does not.

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%, so the single country_code parameter is fully documented in the schema. The description adds no parameter-specific details beyond the schema, so the baseline 3 is appropriate.

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 identifies the resource as a 'country-day censorship classifier score' and adds model provenance, but it uses a noun phrase rather than stating an action and does not distinguish this from sibling tools like get_classifier_info or get_classifier_scope.

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?

There is no guidance on when to use this tool versus alternatives, nor any prerequisite or context for selecting it. The description provides only model metadata, not usage conditions.

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