stock_lhb_jgzz_sina

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is well-covered. The description adds the return type (pandas.DataFrame) and the data source URL, but it does not disclose additional behavioral details such as whether results are paginated, the exact columns returned, or any rate limits. This is acceptable but not rich.

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 follows a logical docstring structure: title, source URL, parameter documentation, and return type. It avoids unnecessary prose, though the title is repeated at the start and in the annotations. Overall, it is appropriately sized and front-loaded with the key information.

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 the low complexity (one parameter, no output schema), the description is adequate for basic invocation but lacks detail about the returned DataFrame's structure (columns, row semantics). It also does not clarify how this tool differs from the many sibling LHB tools, which is important given the large sibling set. The parameter meaning and return type are covered, but the data content and use case context are underspecified.

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 input schema has one parameter 'symbol' with no description (schema coverage 0%). The description fully compensates by explaining that symbol is a choice of {'5': 最近 5 天; '10': 最近 10 天; '30': 最近 30 天; '60': 最近 60 天;}, providing clear semantic meaning for the allowed values. This single parameter is completely documented in the description.

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 states '龙虎榜-机构席位追踪' (Dragon-Tiger List - Institutional Seat Tracking), which clearly identifies the resource. It lacks a direct action verb but the title plus the parameter options (recent 5/10/30/60 days) imply a historical data retrieval function. It partially distinguishes from sibling tools via '机构席位追踪' (institutional seat tracking) as opposed to other LHB variants, but does not explicitly contrast with them.

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 provided about when to use this tool versus the many sibling LHB tools (e.g., stock_lhb_jgmx_sina, stock_lhb_jgstatistic_em). The description only includes parameter choices and a URL without context on use cases, prerequisites, or alternatives. The agent must infer applicability from the name alone.

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