期货期权数据查询（同花顺问财）

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the data source (同花顺问财) and that input is a natural-language question; it does not disclose what the returned data looks like, whether the call is read-only, how pagination behaves, or any external-service limitations. This is a significant gap for an unannotated tool.

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 compact and front-loaded: the first sentence immediately enumerates the data domains, and the second gives input style and source. Every sentence earns its place with no filler or redundant restatement of the tool name.

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 no output schema and no annotations, the description is too sparse to fully prepare an agent. It does not explain the response format, pagination semantics, or the trade-offs between query_futures_data and the many sibling query/select tools. The listed data categories and source are useful, but not sufficient for robust tool selection and invocation.

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%, so the baseline is 3; the schema already documents query, page, and limit. The description's mention of natural-language input merely restates the query parameter's purpose and adds no meaningful semantic detail beyond the schema.

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 uses a specific verb ('查询') and a clearly scoped resource: futures and options data, with explicit subcategories such as quotes, volatility, production/sales, member positions, member rankings, and exercise data. The domain focus distinguishes it from broader siblings like query_market_data or query_basicinfo_data.

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 through '输入自然语言问句' and the futures/options data scope, but it never tells the agent when to prefer this tool over siblings such as select_futures or query_market_data. There are no exclusions or alternative routing hints, so usage guidance is only implicit.

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