fund_fh_rank_em

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, covering the safety profile. The description adds context by specifying the return type (pandas.DataFrame) and the underlying URL, but it does not disclose other behavioral traits such as data volume, pagination, or any special handling. This adds some value beyond annotations but remains limited.

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 three lines: a title identifying the data category, the source URL, and the return type. Every line adds useful information without redundancy. It is front-loaded with the resource name and avoids any verbose or irrelevant content.

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, no-output-schema tool, the description adequately states what it returns (fund dividend ranking) and where it gets it from. It mentions the return type is pandas.DataFrame but does not describe the columns or ranking criteria. Given the simplicity of the tool, this is reasonably complete, though a bit more detail about the DataFrame structure would be beneficial for agent understanding.

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 description does not need to explain any parameter semantics. With no parameters, the baseline score is 4 per the rubric, and the description correctly adds nothing about parameters. The input schema is empty and self-explanatory.

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 returning a fund dividend ranking from Eastmoney (天天基金网), with a URL pointing to the specific page and a return type of pandas.DataFrame. It explicitly states the resource (基金分红排行), making the purpose clear. However, it does not explicitly differentiate from sibling tools like fund_fh_em, though the 'rank' qualifier implies a ranking list rather than general dividend data.

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. The description only provides the source and return type, without mentioning prerequisites, exclusions, or alternative tools. An agent has to infer the intended use solely from the tool name and the basic description.

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