stock_hk_company_profile_em

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

Annotations already establish that this is a read-only, idempotent operation, so the description doesn't need to restate that. It adds the return type (pandas.DataFrame) and a source URL, which provides some context, but it does not disclose the contents of the DataFrame or any other behavioral details.

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 and follows a clear docstring structure with source URL, parameter, return, and rtype. The URL is somewhat lengthy, but each line serves a purpose; it's not padded with fluff.

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 description gives the output type but not the actual fields or structure of the returned DataFrame. It also lacks any context to help an agent decide if this is the right tool among many HK stock tools, making it incomplete for selecting and invoking correctly.

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 schema has no descriptions, so the param docstring ('股票代码' meaning stock code) adds meaning beyond the raw schema. However, it lacks details about the code format, examples, or constraints, leaving the agent with only a general sense of the parameter.

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 states the tool fetches Hong Kong stock company profile data from East Money and returns it as a pandas DataFrame. However, it does not differentiate it from sibling tools like stock_hk_security_profile_em, and the Chinese title largely mirrors the tool name.

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?

There is no guidance on when to use this tool versus alternatives. It does not mention any conditions, exclusions, or scenarios, so the agent gets no help in choosing this tool over related HK stock tools.

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