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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds real value beyond them by disclosing data provenance ('reads the live training sidecars') and a vintage caveat (the retired v2 '0.998 F1' figure had country-tier leakage). Liquidity of results is hinted at but return format, caching, and cost are not discussed.

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 content is dense and almost every clause earns its place — the regime names, the AUC/F1 pairs, the delta AUC, and the v2 caveat are all decision-relevant. It opens with a rhetorical question rather than the operation, and the closing 'HONEST:' restates the earlier 'The honest answer', a minor duplication.

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 carries the full burden of describing returns and does so thoroughly: which regimes, which metrics, the sample basis (127 countries), the generalization gap, and guidance on which metric to cite. An agent can answer the accuracy question correctly without any further source.

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?

The tool takes zero parameters and the schema is fully closed (additionalProperties: false), so there is nothing for the description to disambiguate. Baseline 4 applies and no parameter discussion is needed.

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 precisely what the tool returns: accuracy under three named evaluation regimes (stratified-random, leave-country-out, forward-temporal) with concrete metrics and the generalization gap. That is a specific verb+resource. It does not, however, name the sibling it supersedes — get_classifier_score or get_classifier_info — leaving the 'never quote one number alone' contrast implicit rather than explicit.

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?

It gives an explicit triggering condition ('Use when asked how accurate is the model?') and a directive against the common misuse ('never quote one number alone'). That is clear context, but no named alternative is offered for callers who want a single headline number, and no exclusions are stated.

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