option_lhb_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 the safety profile. The description adds that the tool queries a specific Eastmoney endpoint and returns a pandas.DataFrame, which is consistent with annotations. However, it does not disclose potential rate limits, pagination, or date coverage limitations, so it provides only minimal behavioral context beyond the annotations.

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 well-structured docstring with a clear header line, URL, parameter definitions, and return type. It is scannable and not excessively long, though it redundantly repeats type information already present in the schema (e.g., ':type symbol: str'). The overall structure aids readability, and every line serves a purpose in defining the tool's interface.

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?

The tool has no output schema, so the description should have explained the structure of the returned DataFrame (e.g., columns, index) or what the dragon-tiger list contains. It gives the source URL and parameter choices but leaves the return format implicit. For a data retrieval tool, this is a noticeable gap, although the parameter documentation provides useful context.

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?

Schema description coverage is 0%, so the description carries full responsibility for parameter meaning. It provides explicit choice lists for symbol ('510050', '510300', '159919') and indicator (four specific Chinese option metrics), and describes trade_date as '交易日期' (trading date). The default value '20220121' implies YYYYMMDD format. All three parameters are meaningfully documented, significantly compensating for the schema's lack of descriptions.

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 tool as the Eastmoney data center's option dragon-tiger list, citing the exact URL and naming the resource '期权龙虎榜单'. It specifies three parameters and a DataFrame return, making it distinguishable from other option-related tools in the sibling list (e.g., option_premium_analysis_em). However, it lacks an explicit verb like 'retrieve' or 'fetch', relying on context and the return type.

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?

The description provides no guidance on when to use this tool versus alternatives. It only states the data source and parameters, without mentioning which other tools might be more appropriate for other option data needs (e.g., option_current_em for real-time quotes). No exclusions or contextual triggers are given, leaving the agent to infer usage solely from the name and URL.

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