stock_lhb_yyb_detail_em

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

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, fully covering the safety profile. The description adds modest context: the source URL on East Money and the pandas.DataFrame return type. It does not disclose deeper behavioral traits like rate limits or data coverage, but it does not contradict the annotations either.

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—title line, URL, and a short docstring—with essentially no fluff. A minor deduction applies because the first line exactly duplicates the title annotation, adding redundancy, and the raw docstring format (param/type/return/rtype) is serviceable but not polished prose.

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 is low-complexity: one parameter, no output schema, and strong annotations. The description covers the data source, parameter semantics, and return type (pandas.DataFrame). However, with no output schema present, the description omits any detail about the columns or fields in the returned transaction data, leaving the agent without a clear picture of the data shape.

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?

With 0% schema description coverage, the docstring carries the full burden and compensates well: it explains that symbol is a 营业部代码 (department code), provides an example value "10188715", and instructs that valid codes come from the stock_lhb_hyyyb_em() interface. This goes beyond the bare schema field (type/string/default) by adding semantic meaning, an example, and provenance for valid values.

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's first line restates the title verbatim (东方财富网-数据中心-龙虎榜单-营业部历史交易明细-营业部交易明细), conveying the resource via noun phrases rather than an explicit verb. The tool name and docstring clarify that it fetches historical transaction details for a specific 营业部 code from East Money, but it does not distinguish itself from the many LHB siblings such as stock_lhb_detail_em or stock_lhb_stock_detail_em.

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 explicit when-to-use or when-not-to-use guidance is provided. The only usage hint is that the symbol can be obtained via ak.stock_lhb_hyyyb_em(), which points to a sibling tool but does not explain when this tool is appropriate versus the roughly twenty other LHB-related siblings. The description offers no exclusion criteria or alternative recommendations.

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