stock_lhb_yytj_sina

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds a return type (pandas.DataFrame) and the source URL, but discloses no other behavioral traits such as data update frequency, pagination, or potential inconsistencies. This adds some value but does not go beyond annotations in a significant way.

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 appropriately short and follows a structured docstring format (title, URL, param, return). However, the title phrase is repeated in the return line, and the whole description is a series of fragments rather than a coherent sentence, which slightly reduces clarity.

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?

With no output schema, the description does not specify the columns or the precise content of the returned DataFrame. It provides the source URL and parameter semantics, but an agent would be uncertain about what specific statistics are included (e.g., count, amount, rank). Given the single parameter and simple nature, it is minimally adequate but has clear gaps.

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 only parameter, symbol, is fully explained in the description with a clear mapping of each value to a specific day range (5→最近5天, 10→最近10天, etc.). Since the input schema has 0% description coverage, the description completely compensates by providing all necessary interpretation context.

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 names the resource (龙虎榜-营业部上榜统计) and provides a direct data source URL, indicating it returns statistics on securities department rankings for the Dragon Tiger List. It distinguishes from other LHB tools by focusing on statistical aggregates, 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?

No guidance is given on when to use this tool versus alternatives. The parameter choices (5/10/30/60 days) imply time-range selection, but there is no mention of preferred use cases, exclusions, or comparison with other stock_lhb_* tools.

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