macro_china_consumer_goods_retail

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

Annotations already declare the tool as read-only, idempotent, and non-destructive, covering safety. The description adds the data source URL and the return type (pandas.DataFrame), which is useful context. However, it does not describe the structure of the returned data, frequency, or any potential edge cases, so it only partially goes beyond annotations.

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 concise, consisting of a title, a source URL, and a return type declaration. Every line provides relevant information with no fluff. It is front-loaded with the data name and source.

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 the tool's simplicity (zero parameters, read-only, no output schema), the description provides adequate information: it identifies the data, the source, and the return type. However, it does not specify whether the DataFrame contains historical time series, what columns are included, or the data frequency, which could leave some ambiguity for an agent.

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 coverage is 100% (empty schema), so there are no parameter semantics to explain. Per the rubric, a zero-parameter tool receives a baseline score of 4. The description correctly omits parameter details.

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 providing China's total retail sales of consumer goods data from East Money (东方财富). The name itself is highly descriptive, and the return type (pandas.DataFrame) confirms it retrieves data. However, it lacks an explicit verb like 'get' or 'fetch' and does not explicitly differentiate from other macro_china_* tools beyond the name.

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 other macroeconomic data tools. It simply states the data source and return type. There is no mention of use cases, exclusions, or alternative tools, leaving the agent to infer from the tool name alone.

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