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

Annotations already establish readOnly, idempotent, and non-destructive behavior. The description adds valuable data-behavior context by disclosing that the indicators follow different DSEC reporting cycles: rolling 3-month for unemployment/employment and quarterly for earnings. This helps the agent understand data recency and cadence beyond what annotations provide.

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 compact sentences deliver the source, scope, reporting-cycle nuance, and sample queries with no filler. The core resource and metrics are front-loaded before the explanatory cycle detail.

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 snapshot tool, the description is complete: it names the source, the covered indicators, the reporting cadences, and the typical questions it answers. No output schema exists, but the description sufficiently specifies what data will be returned.

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, so the baseline is 4 and there is no parameter burden for the description to carry. The description correctly focuses on what data the tool returns rather than parameter syntax.

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 (Macau's labour-market snapshot from DSEC) and lists the exact metrics returned: unemployment rate, total employment, and median monthly employment earnings. This clearly differentiates it from sibling tools like macau_tourism, macau_population, and macau_gaming_revenue by domain and data source.

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 concrete user-intent triggers: 'what is Macau's unemployment rate' and 'Macau employment statistics'. It implies this is the tool for Macau labour-market queries, but it does not explicitly state when not to use it or name alternatives, so it stops short of full exclusion guidance.

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