stock_analyst_detail_em

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds minimal behavioral context beyond stating the return type (pandas.DataFrame) and the nature of the data. It does not disclose details like pagination, potential errors, or the meaning of the example URL, but it does not contradict any 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 a compact docstring with a hierarchical title, an example URL, parameter documentation, and return type. It is well-structured and front-loaded with the most identifying information. The example URL is slightly redundant but adds reference context without bloating the text.

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 the return structure, but it only says '具体指标的数据' (data of specific indicators), which is vague. The indicator choices hint at the content (e.g., latest tracked constituents, historical index), but no column names or data shape are provided. For a 2-parameter tool returning pandas.DataFrame, this is a notable gap in 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 descriptions for parameters, but the docstring compensates: analyst_id is explained as the analyst ID obtained from ak.stock_analyst_rank_em(), and indicator lists its allowed choices: {'最新跟踪成分股', '历史跟踪成分股', '历史指数'}. This gives clear meaning beyond the raw schema, although it does not clarify the exact output columns for each choice.

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's hierarchical title '东方财富分析师指数2020最新排行-分析师详情' clearly identifies the resource as analyst details within the Eastmoney analyst index. The :return: line states it provides '具体指标的数据' (data of specific indicators), implying retrieval. It is distinguishable from the sibling stock_analyst_rank_em, which focuses on the ranking list.

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 docstring mentions that analyst_id should be '从 ak.stock_analyst_rank_em() 获取', indicating a dependency on a sibling tool for obtaining valid IDs. However, it does not explicitly state when to use this tool versus alternatives or any exclusions. The usage context is implied through the name and ID source guidance.

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