macro_china_hk_rate_of_unemployment

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

Annotations already declare read-only, idempotent, and non-destructive behavior, so the description doesn't need to repeat that. It adds concrete details: the data source URL and output as a pandas.DataFrame. However, it does not disclose any additional behavioral traits like update frequency, data granularity, or error scenarios.

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 extremely compact, containing only the essential components: data source, URL, return content, and return type. There is no redundant text or filler, and all lines contribute value.

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-argument, read-only tool with annotations and no output schema, the description provides enough information to select and invoke it correctly. It states the source, content, and output type. It lacks details on the DataFrame structure (columns, time range), but this is not critical for basic usage.

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 the description is not required to explain parameters. The baseline score for 0-parameter tools is 4, and the description does not introduce any ambiguity or missing parameter information.

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 identifies the resource (Hong Kong unemployment rate) and source (Eastmoney official data), and explicitly states the return type (pandas.DataFrame) and content (失业率). It distinguishes this tool from sibling macro tools by naming the specific region and indicator, though it lacks an explicit action verb like 'get' or 'fetch'.

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 provides no guidance on when to use this tool versus alternatives (e.g., other Hong Kong macro indicators like CPI or PPI). There are no prerequisites or context hints about suitability for specific queries. Usage is only implied by the tool name and data description.

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