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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered; the description adds genuine context in naming the metric family (LOCO mean F1) and flagging a 'small-sample median caveat', which warns the agent the value can be unreliable in low-data countries. It stops short of describing the return shape or what the caveat means operationally, and the endpoint reference (/v1/classifier/info) reads like leakage from a different tool.

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 compact sentences with no padding, and the core subject (classifier score plus model version) is front-loaded in the first clause. The trailing endpoint path is arguably noise but costs little.

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 one-parameter read tool with no output schema and safety already covered by annotations, the description is near-adequate: it names the metric and a reliability caveat. It is still ambiguous about what the response actually contains and how it relates to get_classifier_info/get_classifier_scope, so an agent could easily misroute.

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 100% schema description coverage and a single documented parameter (ISO 3166-1 alpha-2 code with examples), the schema does the work. The description adds no format, defaulting, or validation detail beyond it, so the 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?

States a specific resource ('country-day censorship classifier score') and pins the model version (GradientBoosting v3.3), and it explicitly rules out a neighbouring concept ('this is not shutdown-onset skill'). However, it does not differentiate itself from close siblings like get_classifier_info or get_classifier_scope, and the phrase 'Read the current LOCO mean F1 and small-sample median caveat from /v1/classifier/info' muddies whether this tool returns a score or the info payload.

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?

Provides one useful exclusion ('this is not shutdown-onset skill'), which tells the agent when not to reach for it, but gives no positive trigger describing when the classifier score is the right call versus the many other score/risk siblings. Usage is implied by the name rather than stated.

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