stock_gpzy_industry_data_em

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. The description adds the source URL and return type (pandas.DataFrame), but does not disclose data granularity, update frequency, or any potential quirks. With annotations present, this is adequate but minimal.

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 three lines with no fluff: a human-readable title, the source URL, and the return type. Every element earns its place, making it highly concise and easy to parse.

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 zero-parameter data-fetch tool with strong annotations, the description is largely complete: it names the data source, the specific dataset, and return format. The only gap is the lack of detail on the DataFrame's columns or structure, but this is not critical given the tool's simplicity.

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, so the schema fully covers parameter semantics. The description correctly notes the return type, which is useful, and the baseline of 4 applies since no parameter explanation is needed.

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 clearly: Eastmoney data center's equity pledge industry data for listed companies. It distinguishes from sibling tools like stock_gpzy_individual_pledge_ratio_detail_em by specifying '行业数据' (industry data), but lacks an explicit verb such as '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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of use cases, prerequisites, or comparisons to sibling equity pledge tools, leaving the agent to infer suitability only from the name.

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