stock_gdfx_holding_teamwork_em

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

Annotations already declare the tool as read-only, non-destructive, and idempotent, so the description's addition of a pandas.DataFrame return type and explicit parameter choices adds some context. It does not disclose data granularity, row/column semantics, or any behavior beyond 'returns top 10 shareholders'. Given the strong annotations, the description adds modest behavioral context, but not enough to fully characterize the tool's output.

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 front-loaded with the dataset name and source URL, followed by concise parameter and return documentation. Every line carries distinct value—title identifies the data, URL provides provenance, param docs clarify the only input, and return type sets expectations. No redundant phrases or vague filler.

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 rich annotations, the basic inputs and safety profile are covered. However, there is no output schema, and the description merely states '十大股东' without specifying columns, row meaning, or the semantics of '协同' (collaboration). The tool could plausibly return data for a specific stock vs. a market-wide view, which is not clarified. The description is adequate for a simple tool but leaves the agent uncertain about result structure and exact usage scope.

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 zero description coverage for the 'symbol' parameter, and the parameter name is ambiguous (it actually represents shareholder category, not a ticker). The description fully compensates by listing all allowed values ('全部', '个人', '基金', 'QFII', '社保', '券商', '信托'), giving clear semantic meaning to an otherwise opaque parameter. This exceeds the baseline requirement for a low-coverage 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 identifies a specific data source and dataset ('东方财富网-数据中心-股东分析-股东协同-十大股东') and states the return type ('十大股东' DataFrame). It clearly differentiates itself from sibling tools by naming the '股东协同' (shareholder collaboration) section, which is distinct from other holding/change/detail variants. However, it lacks an explicit verb like 'retrieves' or 'lists', relying on the 'return' line to imply the retrieval action.

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 provides no guidance on when to use this tool versus alternatives like stock_gdfx_holding_analyse_em or stock_gdfx_free_holding_teamwork_em. It only lists parameter choices and return type, with no mention of use cases, exclusions, or synonyms. The name 'teamwork' implies a specific use case, but the description fails to articulate it, leaving the agent without decision support.

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