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

Annotations already establish that the tool is read-only, idempotent, and non-destructive. The description adds useful behavioral context beyond those hints: the data is monthly, sourced from DSEC, and represents the headline tourism-volume figure. This is meaningful for an agent deciding whether the data matches the user's framing.

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 two tight sentences with no filler. It front-loads the metric name, source, and frequency, then provides concrete example questions. Every element earns its place, including the multilingual aliases which help an agent recognize equivalent user phrasings.

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, read-only, well-annotated tool, the description is nearly complete: it gives the metric, source, frequency, and query intent. The only minor gap is that it does not explicitly specify the return format (e.g., a single number vs. a monthly time series), though 'monthly' and the example questions strongly imply a time series.

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 an empty input schema, so there is no parameter documentation burden. The baseline for zero-parameter tools is 4, and the description reinforces what the query implies—no filtering arguments are needed to retrieve visitor arrivals.

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 exact metric—Macau visitor arrivals—with translations, a source (DSEC), a frequency (monthly), and representative natural-language questions. This makes the tool's purpose immediately clear and distinguishes it from sibling Macau indicators like employment, gaming revenue, and population.

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 by showing the two archetypal queries it answers: 'how many tourists visited Macau' and 'Macau visitor arrivals this month.' It does not explicitly name sibling alternatives or say when not to use it, but for a single-metric data-retrieval tool this is sufficient guidance.

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