stock_industry_pe_ratio_cninfo

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 safety profile is clear. The description adds the return type (pandas.DataFrame) and the data source URL, which provides minor behavioral context beyond the annotations, but it does not describe pagination, rate limits, or any other side effects. This is adequate given the low complexity, but not rich.

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 title, URL, parameter docs, and return type. It is front-loaded with the purpose and contains no fluff. Each line serves a function, keeping it appropriately sized for a simple two-parameter tool.

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?

For a simple read-only tool with two parameters and no output schema, the description covers the source, parameters, and return type. However, it does not specify what columns the returned DataFrame contains, nor does it clarify the date format beyond the default example. This is a viable description but leaves a noticeable gap in the output contract.

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?

Schema description coverage is 0%, so the description must compensate, and it does. It explicitly documents both parameters: symbol with enumerated choices (证监会行业分类, 国证行业分类) and date as '查询日期' (query date). The default value '20210910' implies a date format, but the description does not explicitly state it. Overall, it adds meaning beyond the bare 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 opens with the tool's title '巨潮资讯-数据中心-行业分析-行业市盈率' (CNInfo Data Center - Industry Analysis - Industry P/E Ratio), which clearly identifies the data resource and metric. While no explicit verb like 'get' is used, the noun phrase and URL context make the purpose unambiguous. It distinguishes itself from sibling tools by naming the specific cninfo industry PE ratio dataset.

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 offers no guidance on when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or compare with similar tools like stock_index_pe_lg or stock_a_ttm_lyr. An agent selecting among the many stock valuation tools would have no help from this description.

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