stock_lhb_ggtj_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 agent correctly knows this is a safe, non-mutating operation. The description adds the parameter meaning (days: 5/10/30/60) and the return type (pandas.DataFrame), but does not disclose any additional behavioral traits such as potential missing data, rate limits, or quirks of the Sina source. Given the annotation coverage, this is adequate 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 structured with a title, URL, and docstring-style :param:/:return: sections. It is concise and front-loaded with the purpose. The inclusion of the URL adds reference value but is not strictly necessary, and the return line repeats the title. Overall it is efficient and easy to parse, with only minor redundancy.

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 simple one-parameter read-only tool, the description adequately explains the parameter and return type. However, since there is no output schema, the description does not reveal what columns or structure the returned DataFrame will have, leaving the agent without full knowledge of the output. It also does not mention any context about the data source or what '上榜统计' includes beyond the title. This is a minimal but workable level of completeness.

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 has zero description coverage for the 'symbol' parameter, so the description carries the full burden. The description fully compensates by documenting the valid choices and their meanings: '5' for recent 5 days, '10' for 10 days, '30' for 30 days, '60' for 60 days. This gives the agent everything it needs to correctly select a value, making the parameter semantics highly clear and actionable.

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 opens with '龙虎榜-个股上榜统计' (Dragon-Tiger List individual stock listing statistics), which clearly states the tool's resource and the type of data it returns. It also provides a source URL and parameter details, making the purpose concrete. However, it does not explicitly distinguish this from the many sibling 'lhb' (Dragon-Tiger List) tools, some of which also deal with individual stock statistics, so it lacks differentiation.

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 offers no guidance on when to use this tool versus alternatives such as stock_lhb_stock_statistic_em or stock_lhb_detail_em. There is no mention of prerequisites, scenarios, or which context calls for this specific 'ggtj' (individual stock statistics) variant. The only usage clue is the parameter choices, which are about time ranges, not about when to select this tool.

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