stock_board_industry_cons_em

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive, so the description's lack of safety warnings is acceptable. It adds the data source URL and return type (DataFrame), but no further behavioral context such as pagination, rate limits, or data coverage.

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 title, source URL, parameter, and return type. It is well-structured and front-loaded, though the URL and title are slightly repetitive.

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 single-parameter tool with strong annotations, the description covers the essential return type and parameter semantics. It is incomplete regarding the structure of the returned DataFrame and doesn't give an example symbol, but it meets a minimum viability for selection and invocation.

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 schema has no description coverage, so the description's ':param symbol: 板块名称或者板块代码' is essential. It tells the agent that the symbol can be a name or code, which adds meaning beyond the schema. However, it lacks examples or a note about the default value.

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 resource as industry sector constituents from Eastmoney (沪深板块-行业板块-板块成份), and the return type clarifies it provides constituent data. It is specific about the domain and distinguishes from sibling tools like stock_board_industry_spot_em, though it lacks an explicit verb such as 'get' or 'list'.

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?

No guidance is provided on when to use this tool versus alternatives. It does not mention that this is for constituents as opposed to sector summaries or historical data, nor does it list any exclusions.

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