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

No annotations are supplied, so the description carries the full disclosure burden, and it does more than most: it names the upstream source (IMF World Economic Outlook) and flags that forward years are projections rather than observations, which directly affects how an agent should caveat results. It does not describe return shape, units, currency basis, or update cadence, and with no output schema those remain unknown.

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 sentences, front-loaded with the indicator inventory then the caveat then the use case, with no filler. The indicator enumeration is long but each item is load-bearing information about coverage. Slight trim possible on the trailing sentence, which is more orientation than instruction.

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 zero-parameter reference-data tool with no output schema and no annotations, the definition covers what matters: what data, from where, over what time horizon, and what the numbers mean. The remaining gap is the shape and format of the response — units, series frequency, and whether a single row or a series is returned — which an agent must discover at call time.

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, which is the baseline-4 case; there is nothing for the description to disambiguate. The description's note that data is keyed 'by year' is worth flagging but the schema exposes no year argument, so an agent may wonder whether a year filter exists or whether all years are returned at once.

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 names a specific resource (Peru's key macroeconomic indicators by year) and enumerates the actual fields returned: GDP, real growth, GDP per capita, inflation, unemployment, current account, public debt, population. That is far more concrete than the title alone. It stops short of naming any sibling tool, so differentiation from peru_exchange_rate or peru_investment_snapshot is left to the reader's inference from the indicator list.

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 clear usage context: 'Use it to assess Peru's economic stability and market size,' which tells an agent the decision this data supports. It also clarifies the temporal semantics — years from the current one onward are IMF estimates/projections — which is the main way a caller could misuse the data. It offers no explicit exclusions or named alternatives for overlapping tools such as the exchange rate or investment snapshot tools.

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