stock_lhb_jgzz_sina

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=true, so the safety profile is fully covered. The description adds only the source URL and the return type (pandas.DataFrame), with no disclosure of data scope, freshness, or response shape. Baseline 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?

Compact and front-loaded, with the useful parameter enumeration earning its space. Minor redundancy: the tool name/title is repeated verbatim in the ':return:' line, which adds nothing.

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 single read-only parameter tool with accurate annotations, the definition is mostly adequate, and the parameter values are fully specified. Without an output schema, however, the ':return: 龙虎榜-机构席位追踪 / pandas.DataFrame' line labels the result by name only and gives no sense of the columns/fields an agent will receive.

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 coverage is 0% with no enum, so the description carries the full burden for the single parameter — and it does: it enumerates all four accepted values ('5','10','30','60') with their meanings (最近 5/10/30/60 天). That is meaningful compensation 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 names a specific dataset (龙虎榜-机构席位追踪, institutional seat tracking on the Dragon-Tiger list) and identifies the source URL, so an agent knows exactly what data is returned. However, it gives no differentiation from the many sibling 龙虎榜 tools (stock_lhb_ggtj_sina, stock_lhb_yytj_sina, stock_lhb_jgmx_sina, etc.), so the agent cannot tell which LHB variant to pick.

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?

There is no when-to-use guidance, no prerequisites, and no mention of alternatives. Given the dense cluster of sibling LHB tools, the absence of any routing guidance is a real gap.

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