stock_lhb_jgstatistic_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, so the safety profile is known. The description adds the source URL and return type (pandas.DataFrame), but does not disclose any further behavioral traits such as rate limits, data freshness, or delimiters. It provides some value beyond annotations without contradiction.

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 well-structured as a docstring, with a URL, parameter specification, and return type. It avoids excess prose, though the title phrase is repeated in the return description, making it slightly redundant.

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?

Given one optional parameter, no output schema, and a simple read-only operation, the description includes the key elements: data source, parameter choices, and return type. It omits details like default behavior or output columns, but for this complexity level it is reasonably complete.

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?

The schema provides only a string 'symbol' with a default, while the description specifies the valid choices (近一月, 近三月, 近六月, 近一年). This meaningfully compensates for the schema's 0% description coverage, though it does not explain the period semantics explicitly (e.g., '近一月' = last month).

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's resource: East Money's data center for Dragon-Tiger List institutional seat tracking (机构席位追踪), with a specific URL. The verb is implicit ('returns' via the return type), and the tool is differentiated from siblings by the 'em' source and the statistic name, though not an explicit contrast.

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 guidance is given on when to use this tool versus alternatives. The usage is only implied by the description itself (e.g., choosing a 'symbol' period), with no mention of exclusions or simply when to prefer this over other LHB tools. This is a clear gap for tool selection among many similar siblings.

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