stock_zh_a_gdhs_detail_em

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description does not need to cover basic safety. It adds the data source URL and return type (pandas.DataFrame), which provide some context about output format, but it does not disclose other behavioral traits such as pagination, rate limits, or the specific columns in the returned DataFrame. This is acceptable but not enriched beyond 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 structured as a docstring with source URL, parameters, and return type. It is not verbose and every line adds something, though the URL line is somewhat redundant with the title. It remains well-organized and easy to parse.

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?

With only one parameter and no output schema, the description tells the agent the input (symbol) and the return type (pandas.DataFrame), but it does not detail what columns or data the '股东户数详情' (shareholder count details) actually contains. It is minimally sufficient but leaves ambiguity about the structure and scope of the returned data.

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 one parameter 'symbol' with no description (0% schema coverage), so the description carries the burden. It explains ':param symbol: 股票代码' (stock code) and gives an example URL using 000002, clarifying the expected input format. It also states the return type, adding meaning beyond the bare parameter name.

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 identifies the resource (Eastmoney Data Center - Shareholder Count Details) and provides an example URL, making it clear this tool retrieves shareholder count detail data for a given stock. It lacks an explicit verb like 'retrieve' or 'get', but the intent is clear. It does not explicitly distinguish from sibling tools like stock_zh_a_gdhs, though the 'detail' in the name and description offers some differentiation.

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?

No guidance is provided on when to use this tool versus alternatives. It does not mention sibling tools, exclusions, or specific use cases. The description simply states the data source and parameters, leaving the agent without context for selecting this tool over others.

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