stock_gdfx_free_holding_statistics_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 source URL and return type (pandas.DataFrame), which is useful context beyond annotations. However, it does not disclose any additional behavioral traits such as pagination, rate limits, or error conditions. With annotations present, the description provides marginal extra value, 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 concise and well-structured: a title line, a URL, and a clear parameter/return docstring. It front-loads the purpose and avoids unnecessary verbosity. The URL is extra but useful for source verification. It earns a 4 for efficiency and 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 one parameter, but there is no output schema, so the description must explain the return value. It only says '十大流通股东' (top 10 circulating shareholders) without listing columns or DataFrame structure. Also, with a large family of sibling tools like stock_gdfx_free_holding_detail_em and stock_gdfx_holding_statistics_em, the description does not differentiate what makes this tool distinct. This is a significant gap.

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 0% description coverage, so the description carries the burden. The docstring ':param date: 报告期' clarifies that the `date` parameter means 'reporting period', and the default value '20210630' implies a YYYYMMDD format. This adds meaning beyond the schema property name, but it is minimal—no valid range, format specification, or examples beyond the default.

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 data source and content: '东方财富网-数据中心-股东分析-股东持股统计-十大流通股东' (East Money Data Center - Shareholder Analysis - Shareholder Holding Statistics - Top 10 Circulating Shareholders). It implies a retrieval function by specifying return type pandas.DataFrame. However, it does not explicitly distinguish from closely named siblings like stock_gdfx_holding_statistics_em, so it lacks 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?

There is no guidance on when to use this tool versus alternatives. The description simply provides a URL and parameter documentation, with no mention of appropriate contexts, exclusions, or why one might choose this over the many similar stock_gdfx_* tools. This is a clear absence of usage guidance.

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