stock_gdfx_holding_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 agent knows this is a safe read-only operation. The description adds the data source URL and return type (pandas.DataFrame), but does not disclose any additional behavioral aspects such as pagination, rate limits, or error handling. This adds some context but not rich behavioral detail.

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 with a clear structure: source title, URL, parameter definitions, and return type. Every line serves a purpose without unnecessary fluff. It is appropriately sized for a parameterized data-fetching tool, though it could be slightly more concise by omitting the redundant title line that repeats the annotations.

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 covers the core aspects: data source, parameters, and return type. However, it lacks detail about the returned DataFrame's columns, any prerequisite knowledge (e.g., how to format the date beyond the default), and does not place the tool in context relative to sibling tools. For a simple data-fetching tool with no output schema, this is adequate but with clear gaps in usability guidance.

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?

With 0% schema description coverage, the description fully compensates by explaining each parameter: 'date' as report period, 'indicator' as shareholder type with explicit choices (个人, 基金, QFII, 社保, 券商, 信托), and 'symbol' as holding change with choices (新进, 增加, 不变, 减少). This adds substantial meaning beyond the bare schema defaults.

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 tool as returning top 10 shareholder holding details from East Money's data center, with the return type explicitly stated as '十大股东' (top 10 shareholders). However, it does not explicitly differentiate from sibling tools like stock_gdfx_free_holding_detail_em, which also deal with shareholder holding details, so it stops short of full sibling 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?

The description provides no guidance on when to use this tool versus alternatives. There is no mention of scenarios, exclusions, or references to other tools. While the parameter choices are listed, there is no context for when this specific holding-detail tool is preferred over related shareholder-analysis tools.

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