macro_uk_retail_yearly

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

Annotations already declare readOnlyHint: true, idempotentHint: true, and destructiveHint: false, covering the safety profile. The description adds the source URL (https://data.eastmoney.com/cjsj/foreign_4_9.html) and the return type (pandas.DataFrame), which provides useful context beyond the annotations. No additional behavioral traits like rate limits, pagination, or limitations are mentioned, but none are evident for this zero-parameter data fetch.

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 concise, consisting of a title line, a URL, and docstring-style return information. It is front-loaded with the primary purpose. Minor redundancy exists because the first line mirrors the tool name and title annotation, but the text is otherwise free of waffle and each segment earns its place.

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 tool with no output schema, the description is reasonably complete. It states the data source, the metric, and the return type. It does not specify the date range, column names, or unit, but these are typically implied by the metric name. Given the low complexity, the description provides sufficient context for an agent to invoke the tool correctly.

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 0 parameters, so the input schema is trivially complete. Per the rubric, a baseline of 4 applies when there are no parameters. The description does not need to elaborate on parameter semantics, and it doesn't attempt to invent any.

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 and metric: '东方财富-经济数据-英国-零售销售年率' (East Money - Economic Data - UK - Retail Sales Annual Rate). It distinguishes this from sibling UK macro tools by naming the exact indicator. However, it lacks an explicit action verb (e.g., 'retrieves' or 'returns'), relying on the docstring-style ':return:' to imply data fetching.

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 such as macro_uk_retail_monthly or macro_uk_trade. It neither states use cases nor excludes other tools. The implied usage (need UK retail sales yearly data) is present but not explicitly communicated, so it fails to help the agent make a deliberate selection.

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