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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context: the data is a 'static composite, semi-annual update', and it discloses the cost ('$0.02 USDC per call'). This goes beyond the annotations and helps agents understand data freshness and pricing.

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 front-loaded with the use case and core metrics, but the embedded example ('Emerging market composite — GDP growth 4.2% vs advanced economy 1.7%...') is somewhat verbose and could be shortened without losing critical information. The source and pricing details are useful, but the overall length is slightly bloated for a simple zero-parameter data lookup.

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 burden of explaining the return value. It does so clearly by naming the composite metrics and providing an example. It also includes source, update frequency, and cost, making the tool self-contained for an agent. Minor gap: it doesn't specify the exact country groups or data format, but this is acceptable for a snapshot 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?

The tool has zero parameters and schema coverage is 100% (empty schema), so the baseline is 4 per the rubric. The description does not need to compensate for any parameter gaps; it appropriately focuses on the data returned rather than parameter semantics.

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 clearly states a specific verb ('Returns') and resource ('IMF WEO macro composites') along with the exact metrics (GDP growth, inflation, current account balance) and use cases (international expansion brief, country risk assessment, board-level presentation). It is easily distinguishable from siblings like get_inflation_benchmark or get_macro_market_signal due to its explicit IMF WEO and country-group focus.

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 provides explicit when-to-use contexts ('Use when providing global macro context for an international expansion brief, country risk assessment, or board-level economic outlook presentation'). It does not name alternatives or when-not-to-use, but the clear use cases and unique data source give sufficient guidance for a zero-parameter tool.

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