stock_hk_growth_comparison_em

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds some value by specifying the return type (pandas.DataFrame) and the source URL, but it does not disclose additional behavioral traits such as data freshness, pagination, or rate limits. Since annotations cover the core safety aspects, this is adequate but not rich.

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 concise and well-structured, with a title, source URL, and param/return docstrings. It avoids unnecessary fluff. The URL might be considered extra but serves as a useful reference. No redundant sentences are present.

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, read-only tool, the description provides the return type and param meaning, which is adequate for a basic call. However, it does not describe the contents of the DataFrame (columns or dimensions of growth comparison) or the meaning of the default symbol '03900'. Since there is no output schema, more detail about the return structure would improve completeness.

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 description includes a docstring for 'symbol' as '股票代码' (stock code), which adds meaning beyond the schema's bare string type and default value. However, it does not specify the expected format (e.g., leading zeros, 5-digit HK code) or how to obtain valid symbols. With schema description coverage at 0%, the description partially compensates but leaves gaps.

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 resource (HK stock industry comparison) and the specific aspect (growth comparison) via the Chinese title '东方财富-港股-行业对比-成长性对比'. It distinguishes from sibling tools like stock_hk_valuation_comparison_em and stock_hk_scale_comparison_em. However, it lacks an explicit verb like 'get' or 'fetch', relying on the implicit return type to convey 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?

No guidance is provided on when to use this tool versus alternatives. The description does not mention exclusions, prerequisites, or scenarios where another tool (e.g., stock_zh_growth_comparison_em for A-shares) would be more appropriate. The only context is the title and URL, which imply the data source but not usage conditions.

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