stock_zh_a_gdhs

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds context beyond annotations by naming the source URL, specifying the return type (pandas.DataFrame), and explaining the parameter format, but it doesn't disclose additional behavioral traits like rate limits, data freshness, or error conditions. With such rich annotations, this is adequate but not extensive.

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 a compact, well-structured docstring that front-loads the source/title, then uses :param: and :return: tags. It avoids unnecessary prose; the URL and format note are useful. It is slightly cryptic due to the Chinese language and mixed docstring format, but each element earns its place—just not quite a perfect score.

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?

This is a low-complexity tool with one parameter and no output schema. The description covers source, data content, parameter values/format, and return type. It doesn't describe the DataFrame columns, but the name '股东户数' and source make the content clear. For a simple query tool with rich annotations, this is sufficiently complete, though stating it returns all A-shares (rather than a single stock) would make it fully explicit.

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 only provides a string type with a default value and no description. The description compensates fully by explaining that symbol accepts either '最新' or a quarter-end date formatted as YYYYMMDD (e.g., 20230930), which is crucial for the agent to pass the correct value. This adds significant meaning beyond the bare 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 data source (东方财富网/数据中心的特色数据) and the dataset (股东户数/shareholder counts), and the :return:/:rtype: fields specify it returns a pandas DataFrame. However, it lacks an explicit verb like 'fetch' or 'retrieve', and doesn't differentiate itself from closely related siblings such as stock_zh_a_gdhs_detail_em, so it falls short of a 5.

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 :param symbol: description explains the two allowed values ('最新' and '每个季度末' with YYYYMMDD format), which is helpful for correct invocation. However, there is no guidance on when to choose this tool over sibling tools (e.g., when to use stock_zh_a_gdhs_detail_em for more granular data), so the 'when vs alternatives' aspect is missing.

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