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

Annotations already carry the safety profile (readOnlyHint=true, idempotentHint=true, destructiveHint=false), so the bar is lower. The description adds value beyond annotations by disclosing the mathematical transformation behavior, the matrix-mode expansion when target is omitted, and the critical non-obvious return trait: 'USD purchasing-power equivalents, not currency-converted amounts.' This prevents a common misuse.

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?

Three dense sentences, each earning its place: the core operation with formula, the batch-mode variation, and the return-semantics clarification. The most decision-relevant information (what it computes and how) is front-loaded, with zero filler words.

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?

Given the output schema exists and annotations are rich, the description need not explain return structures or safety. It covers the formula, both invocation modes, and return semantics. Minor gaps remain: no guidance on choosing among the 8 source datasets and no edge-case behavior (e.g., home == target), but these are minor for a read-only calculation tool.

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 coverage is 100%, so the baseline is 3. The description adds genuine meaning beyond the schema by relating home and target through the ratio (I_target / I_home), giving the agent a conceptual model of how the parameters interact. The omit-target behavior is somewhat redundant with the schema's target description, but the formula is a real increment.

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 verb ('Convert') + resource ('base salary') + purpose ('cost-of-living-fair local equivalent'), backed by the exact formula S_local = S_base × (I_target / I_home). It differentiates itself from close siblings like calculate_ppp and calculate_wage_bands by clarifying it returns salary equivalents, not indices or bands, and explicitly notes it is not a currency conversion.

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 description gives clear context for the two modes: single-target conversion versus omitting target to 'receive a localized salary matrix across every country' with the use case 'pricing one offer across many markets.' However, it does not name sibling alternatives (e.g., calculate_ppp, calculate_wage_bands) or state when-not-to-use, so routing between related tools is left to inference.

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