stock_industry_change_cninfo

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 that it queries the specific CNInfo p_stock2110 endpoint and returns a pandas DataFrame, but it does not describe authentication, rate limits, pagination, or the exact structure of the returned data.

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 structured docstring with one line for purpose, a source URL, and parameter/return definitions. It is not overly long, though the first line duplicates the annotation title. The information is front-loaded with the main purpose in the first line.

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 has no output schema, so the description should explain the return values. It states the return type (pandas.DataFrame) and content (行业归属的变动情况), but does not specify the DataFrame columns or any additional behavior such as date boundaries or error conditions. Given the simplicity of the tool and read-only annotations, the description is adequate but leaves some ambiguity about the output structure.

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 input schema provides only names and defaults for the three parameters, with 0% description coverage. The description compensates by defining each parameter: symbol (股票代码), start_date (开始变动日期), and end_date (结束变动日期), making their purpose clear. It does not state the date format explicitly, but the defaults (e.g., 20220713) imply YYYYMMDD.

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 explicitly states '查询 p_stock2110 接口' (query the p_stock2110 interface) and describes the resource as '上市公司行业归属的变动情况' (changes in industry attribution of listed companies). This clearly identifies the tool as a query for industry attribution changes from CNInfo, distinguishing it from sibling tools like stock_industry_category_cninfo and stock_industry_pe_ratio_cninfo.

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 context that this tool queries industry changes for a stock over a date range, but it does not mention any alternative tools, when-not-to-use conditions, or prerequisites. The usage is implied by the parameter definitions but not explicitly guided.

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