get_option_lhb

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

Annotations already declare readOnlyHint=true and openWorldHint=false, and the description adds return content details. However, it does not disclose additional behavioral aspects like data freshness, pagination, or access restrictions. It adds some context but not rich behavioral transparency.

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 single, front-loaded sentence that states the purpose and output without unnecessary words. It is appropriately sized and compact.

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?

With only 2 parameters, high schema coverage, and readOnly annotations, the description is mostly complete. It mentions the return content, which is useful since there is no output schema. However, it lacks usage guidance or caveats, so a 4 is appropriate.

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 100% for both parameters (symbol and date), so the schema fully documents them. The description does not add extra meaning beyond the schema, which is the baseline for high 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 the tool retrieves option dragon-tiger list data and specifies the outputs (buy/sell volumes and position rankings per seat). It explicitly uses '期权' (options) to distinguish from the generic dragon-tiger siblings like get_dragon_tiger_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?

The description implies usage for option-specific dragon-tiger data through the tool name and '期权' marker, but it does not explicitly state when to use this versus alternatives or provide exclusions. No when-to-use or when-not-to-use guidance is given.

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