stock_lhb_jgmmtj_em

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds the return type (pandas.DataFrame) and source URL, but it does not disclose additional behavioral traits such as date range limitations, data granularity, or response columns. No contradictions, but minimal added value beyond annotations.

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, consisting of a title, a URL, and a brief docstring. It is front-loaded with the core purpose, and there is no wasteful prose. However, the docstring largely restates the title and parameters, and the URL could be considered extra.

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?

With no output schema, the description should explain what the returned DataFrame contains (columns, rows, and meaning). It only says '机构买卖每日统计' without specifying fields like stock code, buy amount, sell amount, or net buy. Given the numerous sibling LHB tools, this description is insufficient for an agent to predict the output or differentiate the tool.

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 schema description coverage at 0%, the description must compensate, but it only repeats the parameter names and types ('start_date: 开始日期') without explaining format, inclusivity, or examples. The default values (20240430) implicitly suggest YYYYMMDD format, but this is not explicitly stated.

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 identifies the tool as returning Eastmoney's daily institutional buy/sell statistics from the Dragon Tiger List, which helps distinguish it from other LHB tools such as stock_lhb_detail_em or stock_lhb_jgstatistic_em. However, it lacks an explicit verb like 'fetch' or 'get', relying instead on a noun phrase.

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 guidance on when to use this tool versus the many sibling LHB tools, nor any contextual or alternative recommendations. The description only provides parameters and a source URL, giving no indication of preferred use cases or exclusions.

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