stock_info_a_code_name

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, covering safety expectations. The description adds minimal context beyond the return type and a broad list scope, such as what columns might be included or that it is a reference list. No contradiction with annotations exists.

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 very short and to the point, consisting of a title and a return type annotation. It is front-loaded and efficient, though slightly under-specified for the return data's actual content.

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?

With no parameters, no output schema, and a simple list operation, the description is adequate but lacks details about the returned DataFrame's columns (e.g., whether it includes code and name only) or any caveats like market coverage. Sibling confusion is possible without more specificity.

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 tool has zero parameters and the schema is empty, so parameter semantics are trivially complete. Per the rubric, a baseline of 4 applies for 0-parameter tools, and the description does not need to explain nonexistent parameters.

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 states '沪深京 A 股列表' (Shanghai, Shenzhen, Beijing A-share list) and return type as DataFrame, making it clear the tool provides a list of A-shares. However, it does not differentiate from sibling tools like stock_zh_a_spot_em or stock_bj_a_spot_em, and the description is essentially a noun phrase without an explicit verb like 'get' or 'fetch'.

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 only states what it returns, not the context or exclusions, and no sibling comparisons are mentioned.

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