stock_us_famous_spot_em

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds behavioral context by specifying the return type (pandas.DataFrame) and that data is real-time ('实时行情'), plus the source URL. However, it does not disclose details like update frequency, pagination, or how invalid symbols are handled, and it does not contradict the annotations.

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 well-structured as a docstring: source, URL, parameter, return, and return type. It avoids unnecessary prose. The only minor issue is that the first line duplicates the annotation title, but it still serves as a clear header. Overall, every sentence contributes value.

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 (one parameter, no output schema), and the description covers the source, parameter, and return type. However, it does not clarify what columns the DataFrame contains, what constitutes '知名美股', or whether the result is a list of all stocks in a category. Given the lack of an output schema, a bit more detail on the returned data would be beneficial, but the description is minimally adequate.

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 only defines 'symbol' as a string with a default value, lacking an enum or description. The description compensates by enumerating the valid choices: {'科技类', '金融类', '医药食品类', '媒体类', '汽车能源类', '制造零售类'}, which is essential for using the parameter correctly. It also labels it as a 'choice' (choice), adding meaning beyond the raw 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 clearly identifies the resource as '知名美股' (famous US stocks) from 东方财富网 and states the return is '知名美股实时行情' (real-time quotes), which effectively conveys the tool's function. It distinguishes from siblings like stock_hk_famous_spot_em by the explicit '美股' (US stock) and '知名' (famous) scope. However, it lacks an explicit action verb like 'get' or 'fetch', relying on the return statement to imply retrieval.

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 provides no guidance on when to use this tool versus alternatives such as stock_us_spot_em or stock_us_pink_spot_em. It only gives the source and parameter choices without contextual cues like 'use this for famous US stock quotes by category'. There is no exclusion or comparison to sibling tools.

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