stock_gdfx_free_holding_detail_em

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds useful context by specifying the return type (pandas.DataFrame) and the data source URL, but it does not disclose details like data freshness, pagination, or column specifics, which would be valuable for a financial data tool.

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 front-loaded with the tool's purpose. It includes a URL for reference and a compact docstring, but the layout is a bit unstructured (mixing Chinese title, URL, and parameter notes). Still, every line serves a purpose.

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 specifies the return type (DataFrame) and the general content (top 10 floating shareholders), but lacks details on columns, index, or any peculiarities of the data. Given the simple one-parameter interface and good annotations, this is adequate but not rich. The lack of an output schema increases the need for more detail.

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 the parameter name and default value, but the description's docstring ('报告期' for date) adds semantic meaning, clarifying it is the reporting period. With only one parameter and 0% schema description coverage, the description adequately compensates.

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 resource (东方财富网-数据中心-股东分析-股东持股明细) and the specific data (十大流通股东), making the tool's purpose unambiguous. It distinguishes itself from sibling tools by naming the exact category of shareholders, though it doesn't explicitly compare to alternatives.

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 about when to use this tool versus the many similar sibling tools (e.g., stock_gdfx_free_holding_analyse_em, stock_gdfx_holding_detail_em). The description gives the data source URL and parameter docstring but omits context like typical use cases, prerequisites, or exclusions.

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