stock_individual_basic_info_us_xq

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds useful context: it notes the use of an xq_a_token for authentication and that an APIError is raised when a valid login is required. However, it does not describe pagination, rate limits, or behavior on invalid symbols. No contradiction with annotations is present.

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, with a title, source URL, and parameter/return/exception documentation. The URL is useful for verification. It is not verbose and every line contributes to understanding. The only minor issue is the mixing of a raw URL into the prose, but it does not hamper readability.

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?

Given the absence of an output schema, the description should describe the return data in more detail. It states the return type (DataFrame) and content (公司简介/company profile), but does not list the columns or fields included. The authentication requirement is disclosed, and annotations cover safety, but for a tool with no output schema, the description lacks enough detail to fully predict the return structure. It is adequate for a simple lookup but not thorough.

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?

With schema description coverage at 0%, the description's docstring carries the full burden, and it compensates well: each parameter is given a meaningful explanation—symbol as 证券代码 (securities code), token as 雪球财经的 xq_a_token, and timeout as 设置超时时间. The example URL further clarifies symbol format (e.g., NVDA). It would benefit from noting the required format for US symbols, but it is adequate for basic usage.

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 opens with '雪球-个股-公司概况-公司简介' (Xueqiu - Individual Stock - Company Overview - Company Profile), clearly stating the resource and data type returned. It specifies a return type of pandas.DataFrame and includes a source URL. However, it does not explicitly mention 'US' stocks within the description, relying on the tool name to distinguish from sibling tools like stock_individual_basic_info_xq and stock_individual_basic_info_hk_xq.

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. It does not mention that it is for US stocks, nor does it reference sibling tools for Hong Kong or A-share stocks. The only context is the URL and the tool name, which implies but does not state the scope.

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