stock_gdfx_free_top_10_em

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 the data source (East Money URL) and the return type (pandas.DataFrame), but does not disclose data lineage, column contents, or any potential quirks like pagination or historical coverage. It provides context beyond annotations but not rich behavioral detail, so a 3 is appropriate.

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 docstring of four lines: a title, a reference URL, two parameter explanations, and return type. It is front-loaded with the title, each section is clearly labeled, and there is no extraneous prose. The URL duplicates the title but serves as a citation. This is a model of concise structure.

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 with two optional parameters and no output schema. The description provides the return type and param meanings, but leaves gaps: it does not describe the DataFrame columns, distinguish free/circulating shareholder data from other shareholder data, or explain how to choose this over the many sibling gdfx tools. Given the annotation safety coverage, it is adequate for a basic read operation but lacks context for nuanced selection.

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 provides only default values with no descriptions (0% schema coverage). The description supplements this with ':param symbol: 带市场标识的股票代码' and ':param date: 报告期', clarifying that symbol requires a market prefix and date is the reporting period. This adds meaning, but the explanations are terse and do not specify exact formats (e.g., YYYYMMDD) beyond the defaults, so it does not fully compensate.

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's title and first line state '东方财富网-个股-十大流通股东' (East Money individual stock top 10 circulating shareholders), and the URL points to the detailed shareholder research page. The ':return:' field states it returns 十大股东 as a DataFrame, making the purpose clear. However, there is no explicit verb like 'get' or 'retrieve', and it does not explicitly differentiate from sibling tools like stock_gdfx_top_10_em in the description text.

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 lacks any guidance on when to use this tool versus alternatives. It does not mention any preconditions, typical use cases, or exclusions. The only contextual clue is the URL example for symbol SH688686, which implies usage but does not state when to select this over similar gdfx tools.

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