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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and closed-world, so the safety profile is covered. The description adds useful scope facts (base year, 16 sectors, monthly frequency) but says nothing about pagination, missing periods, or how sector-level results differ from general-level results.

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?

Two sentences, front-loaded with the dataset identity and scope, with no filler. Slightly terse for a data tool but every clause carries information.

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 an output schema present, the description need not explain return values, and the fully documented schema plus annotations cover the mechanics. What remains thin is routing guidance relative to the many sibling indicator tools, but the core scope is complete.

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 description coverage is 100%, so start/end formats, sector examples and the measures vocabulary are already fully documented in the schema. The description only reinforces the sector dimension ('16 sectores') and adds no format or default detail beyond what the schema states, which is the baseline-3 case.

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 the specific dataset (EMAE, base 2004=100) and states its scope: general level since 2004 plus 16 sectors, monthly, leading GDP. That is a clear resource statement an agent can match to a request for Argentine monthly activity data, though it does not explicitly contrast itself with siblings like industrial_production or construction_costs.

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?

There is no when-to-use or when-not-to-use guidance and no named alternative. The phrase 'indicador mensual que anticipa el PIB' hints at context but never tells the agent to prefer compare_series for cross-dataset comparison or latest_indicators for the most recent print.

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