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

With annotations already marking this as read-only, idempotent, and non-destructive, the description adds useful behavioral context: the metric is quarterly and sourced from DSEC. This goes beyond the structured annotations without contradicting them.

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 concise sentences with no filler. It front-loads the core metric, includes useful multilingual labels, and ends with a concrete query example.

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 no-parameter, simple data lookup with rich annotations, the description is nearly complete: it names the metric, frequency, source, and example question. It does not spell out the exact return shape, but that is a minor gap given the tool's simplicity.

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?

There are zero parameters and schema coverage is 100%, so the description carries no parameter burden. The baseline of 4 applies because there is nothing missing.

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 the resource (Macau's total resident population), the verb ('Answers'), and the data source (DSEC). It distinguishes itself from sibling Macau tools like macau_employment and macau_tourism by naming the specific population metric.

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 an explicit query the tool answers: 'what is Macau's population'. It does not mention when not to use it or name alternatives, but for a simple no-parameter lookup this is clear context.

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