stock_zh_valuation_baidu

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds the data source (Baidu), the data category (financial statements/valuation), and the return type (pandas.DataFrame). It does not disclose additional behavioral traits such as rate limits or permissions, but none are implied. The description is consistent with annotations, so no contradiction.

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 well-structured: a title line, an example URL, and a param list. Each element serves a purpose—the title states the domain, the URL shows the source, and the param docs explain inputs. It is slightly redundant with the tool name but remains efficient and front-loaded.

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?

There is no output schema, so the description should cover return structure. It states the return type as pandas.DataFrame and labels it '估值数据' (valuation data), but does not describe expected columns, index, or the effect of period choices. For a simple query tool, this is adequate but not comprehensive, leaving room for ambiguity about the returned data format.

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 has 0% description coverage and no enums, but the description fully documents all three parameters: symbol (股票代码), indicator (choice of five metrics), and period (choice of five ranges). It also provides defaults in the schema, and the description adds meaning by enumerating the exact allowed choices. It does not explain the meaning of each indicator or period, but the choices are self-explanatory for the domain.

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 the tool as '百度股市通-A股-财务报表-估值数据' (Baidu Stock Connect A-share financial statement valuation data), with an example URL. It distinguishes from siblings like stock_hk_valuation_baidu and stock_us_valuation_baidu by specifying 'A股' (A-shares). However, it lacks an explicit verb such as 'retrieve' or 'get', relying on the noun phrase to imply the 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 context (Baidu source, A-share market, valuation metrics) that implies when this tool is appropriate. It does not explicitly state when to prefer this over alternatives like stock_zh_valuation_comparison_em or stock_value_em, nor does it give when-not guidance. Usage is implied rather than direct.

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