公司股东股本查询（同花顺问财）

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

With no annotations, the description carries the burden of behavioral disclosure. It adds useful context by stating the data source and that natural-language input is supported, but it does not describe return format, pagination behavior, or any limitations.

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 compact and front-loaded: it states what data is covered, then the input style, then the data source. Every sentence earns its place with no redundancy.

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 natural-language query tool, the description covers domain, input style, and provenance, and the schema fully documents parameters. It could be improved by stating what the response looks like, since there is no output schema, but the core selection and invocation context is clear.

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 input schema already covers all three parameters with descriptions, including the fact that 'query' can be rewritten into a standard financial question. The tool description adds little beyond confirming natural-language input, so the baseline of 3 is appropriate.

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 names a concrete action ('查询') and a specific resource: equity/capital-structure data, including shareholding structure, shareholder counts, top shareholders, major holders, and actual controllers. This distinguishes it from market, industry, macro, and fund siblings, though it does not explicitly cite an alternative sibling.

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 makes it clear that the tool is for natural-language equity/shareholder questions and identifies the data source, so the intended context is inferable. However, it gives no explicit when-to-use/when-not-to-use guidance or named alternatives among the many query_* siblings.

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