macro_china_hk_gbp_ratio

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

The annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is well covered. The description adds a useful detail by specifying the return type as pandas.DataFrame and provides the data source URL, but it does not go beyond that to describe data granularity, update frequency, or any other behavioral nuances.

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 short and to the point, with three concise parts: the title, the source URL, and the return type. It avoids unnecessary verbosity, though the first line merely echoes the title, which is slightly redundant. Overall, it is efficient and front-loaded.

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?

Given that the tool has no parameters and no output schema, the description provides the essential facts: what data it returns (Hong Kong GDP YoY) and the return format (pandas DataFrame). However, it is minimal—no explanation of the data's columns, the time series nature, or any limitations, which may leave an agent uncertain about the exact structure of the returned data.

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 the schema is an empty object. According to the baseline for zero parameters, the description does not need to add parameter-level detail, and it correctly omits any. The lack of parameters is clearly communicated through the schema, and the description does not introduce any confusion.

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 tool as retrieving Hong Kong GDP year-over-year data from East Money, and the return type is specified as a pandas DataFrame. However, it lacks an explicit verb like 'fetch' or 'return'—the description is largely a restatement of the title, which itself already provides the same information.

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 guidance on when to use this tool versus alternatives, no description of ideal use cases, and no mention of prerequisites or exclusions. Sibling tools like macro_china_hk_gbp or macro_china_hk_cpi_ratio are not referenced, leaving the agent without any context for selecting this specific tool.

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