行业数据查询（同花顺问财）

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

No annotations are provided, so the description carries the full burden. It usefully discloses the data source (同花顺问财) and the natural-language modality, but it does not describe the output format, pagination behavior, or any limitations of the underlying service.

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: one sentence for scope, one for input mode, and one for data source. Every sentence contributes non-redundant information, and the core action is front-loaded.

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?

The tool is simple and its parameters are fully documented, but there is no output schema and the description does not explain what the response looks like. In a family of many sibling data-query tools, the lack of explicit differentiation also weakens completeness.

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?

Schema description coverage is 100%, so the schema already documents all three parameters. The description adds no meaningful parameter semantics beyond what the schema states about natural-language queries and page/limit defaults.

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 specific action ('查询') on a distinct resource ('行业数据') and enumerates concrete content categories such as 估值、财务、盈利、行情、板块排名. It is clear overall, but the catch-all '等数据' and overlap with sibling tools like query_finance_data and query_market_data keep it from fully distinguishing itself.

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 scope statement implies use for industry-level data questions and the natural-language input mode is clear. However, no explicit when-to-use, when-not-to-use, or sibling alternatives are given, so an agent must infer the choice among many similar query_*data tools.

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