stock_lhb_stock_statistic_em

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

Annotations already declare readOnlyHint=true, destructiveHint=false, establishing the safety profile. The description adds the source URL and that it returns a pandas.DataFrame, but does not disclose data freshness, pagination, or any caveats. With annotations covering the risk profile, the added behavioral context is minimal but not zero, so 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 a compact docstring with five lines covering title, URL, parameter, return type, and return description. It is front-loaded with the title and source, making the purpose immediately visible. The URL line feels slightly disjointed, but overall it is concise and free of fluff.

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 simple one-parameter read-only tool with no output schema, the description is minimally adequate. It covers the parameter and return type, but does not describe the structure of the returned DataFrame or any limitations. The domain-specific name and title help, but an agent might need more detail on what columns or rows to expect. It is 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?

The input schema has no description for symbol and no enum, so the description is the only source of semantic meaning. It explicitly lists the allowed values (近一月, 近三月, 近六月, 近一年) and the type, which is essential for correct invocation. It does not explain the exact date ranges implied by these values, but they are self-explanatory enough. This significantly compensates for the schema's 0% coverage.

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 states it provides 个股上榜统计 (individual stock listing statistics) from Eastmoney's Dragon-Tiger List, identifying the resource and scope. It distinguishes from sibling tools like stock_lhb_stock_detail_em by focusing on aggregate statistics per stock. However, the verb is implicit (it does not explicitly say 'get' or 'retrieve'), so it falls short of a perfect 5.

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 only gives the data source and parameter choices, without mentioning alternative tools or contexts where a different LHB tool would be more appropriate. This is a clear gap.

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