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

Annotations already declare readOnlyHint and openWorldHint=false, so safety is covered; the description goes further by naming the three evaluation regimes and their purpose, listing returned metrics, and explicitly flagging that v2 had country-tier leakage and is not a live claim. That methodological caveat is exactly the kind of context annotations cannot carry.

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?

Front-loaded with the core question (what the tool answers) and each subsequent sentence adds distinct value: the regimes, the returned metrics, and the honesty caveat. Slightly chatty phrasing, but no filler worth cutting.

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 parameters and no output schema, the description carries the return-value burden and does so well by enumerating AUC, F1, the generalization gap and citation guidance. What remains unaddressed is how this tool relates to its two classifier siblings.

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?

Zero parameters, so there is nothing to disambiguate and the baseline is 4. The description does not need to compensate for any schema gap.

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 verb+resource: reading the classifier's evaluation sidecars across three named regimes, plus the metrics returned. Clear enough to act on, but it never differentiates itself from the sibling tools get_classifier_info and get_classifier_score, leaving the agent to guess which classifier endpoint to pick.

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?

Gives real usage guidance about interpreting results ('Never quote one number alone', 'which metric to cite for which use'), but gives no when-to-use vs the classifier siblings (get_classifier_info, get_classifier_score) or when-not to call it. Usage is implied by content rather than routed.

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