stock_a_gxl_lg

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=true. The description adds the specific data source URL and confirms it returns a pandas DataFrame. It doesn't add details about network requirements, rate limits, or data freshness beyond the URL, but with annotations covering safety, 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 fairly concise: title, URL, param list, and return type. It's structured as a docstring, which is efficient. It includes necessary info without excessive fluff. Could be slightly more organized but overall earns its place.

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 optional parameter and no output schema. The description provides the URL and return type, which is helpful. However, it doesn't mention what the DataFrame contains beyond 'A股股息率' (e.g., columns, time series vs point-in-time), and doesn't state any limitations or examples. Given the simplicity, it's adequate but not comprehensive.

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 coverage is 0%, but there is only one parameter with a default value. The description does list the enumerated choices in the :param symbol: line, which adds value beyond the schema (which only shows default). However, it doesn't explain the meaning of each choice in depth, but the choices are self-explanatory (e.g., 上证A股, 深证A股). With one simple param and defaults, baseline 3 is reasonable.

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 fetching A-share dividend yield data (股息率) from 乐咕乐股 (legulegu.com), with a specific URL. It includes a parameter choice for the market segment. However, it doesn't explicitly differentiate from sibling tools like stock_hk_gxl_lg or stock_a_all_pb, though the name and description are quite specific to A-share dividend yield.

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 implies usage for retrieving A-share dividend yield for a chosen market segment via the symbol parameter. It does not state when to use this instead of other related tools like stock_hk_gxl_lg (HK dividend yield) or stock_a_all_pb (A-share PB). No explicit alternatives or exclusions are given, but the context is clear enough.

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