stock_comment_detail_scrd_focus_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 the return type (DataFrame) and a source URL, which gives some context, but does not disclose pagination, rate limits, or other behavioral aspects. No contradiction with 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 5 lines, front-loading the source and URL. It is efficient and free of fluff, though the first line duplicates the title from annotations, which is 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?

With no output schema, the description explains that it returns a DataFrame with the market heat/user interest index. For a single-parameter retrieval tool with clear annotations, this is largely sufficient, though it could specify the DataFrame's columns or whether historical data is included.

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?

Schema coverage is 0%, so the description bears the burden. It defines `symbol` as '股票代码' (stock code) with type str and includes a URL example (600000), providing basic semantic meaning. However, it does not specify the expected format, market prefix, or constraints, leaving some ambiguity for the agent.

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 identifies the specific data source ('东方财富网-数据中心-特色数据-千股千评-市场热度-用户关注指数') and return type (pandas.DataFrame), making the tool's function clear. However, it lacks an explicit verb like 'get' or 'fetch', and the first line is essentially a breadcrumb phrase. It distinguishes from siblings by naming the exact metric (user attention index).

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 guidance is provided on when to use this tool versus related tools such as stock_comment_detail_scrd_desire_em or stock_comment_em. The description only states parameters and return type, leaving the agent to infer usage context without any comparative direction.

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