问财选美股（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 of behavioral disclosure. It does disclose the natural-language screening behavior, supported multi-condition combinations, and data source. However, it does not describe the return format, pagination behavior, rate limits, or error conditions, which are relevant for an agent invoking the tool.

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: two sentences that front-load the tool's purpose and capabilities, then cite the data source. There is no redundancy with the tool name or title, and every sentence adds meaningful information.

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 simple 3-parameter tool with one required parameter, the description covers the core use case and data source. However, with no output schema and no annotations, the description should ideally clarify what the agent will receive after a successful query, such as the structure or fields of the returned stocks, making completeness adequate but not strong.

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 query, page, and limit. The description reinforces that query is a natural-language screening statement and adds that multiple conditions can be combined, but it does not provide additional parameter-level syntax or format details beyond the schema.

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 states a specific action and resource: '通过自然语言查询进行美股筛选' (filter US stocks via natural-language queries). It also lists concrete screening dimensions such as market indicators, financial indicators, industry concepts, earnings forecasts, and research ratings, which clearly distinguishes it from sibling tools like select_astock and select_hkstock by market scope.

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?

Usage context is implied: use this when the user wants to screen US stocks using natural language, and the data source is provided. However, there is no explicit when-to-use/when-not-to-use guidance, nor mention of alternatives such as select_astock or select_hkstock, leaving sibling selection to the agent's inference.

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