stock_zh_kcb_report_em

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds that the function returns a pandas DataFrame and accepts page-range parameters, which gives some behavioral context. However, it does not disclose potential rate limits, the nature of the 'report content' beyond a vague label, or whether the data is scraped in real-time. With annotations handling safety, this seems 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 concise, structured as a docstring with distinct sections for description, URL, parameters, and return. There is slight redundancy (repeating '科创板报告内容' as the description and return), but the text is short and easy to parse. Every element except the title repetition serves a purpose.

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?

The tool is simple (2 parameters, no output schema), and the description states the return type (DataFrame) and the resource. However, it does not elaborate on what the DataFrame contains (columns, report types) or any caveats about the data source. For a paginated report retrieval tool, this is a modest gap but not severe.

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 must compensate. It does so by explicitly documenting both parameters: from_page (start page) and to_page (end page). This adds meaningful semantics beyond the schema's type/default information. However, it lacks details on page size, ranges, or value constraints, but the basic meaning of each parameter is clear.

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 indicates the tool retrieves STAR Market (科创板) report content, with a source URL (data.eastmoney.com/notices/kcb.html) that grounds it in a specific data source. Although the verb is implicit rather than explicit (e.g., 'fetch'), the combination of the title, name, and parameters makes the purpose reasonably clear. It does not explicitly differentiate from sibling tools like stock_research_report_em or stock_zh_kcb_daily, but the specific reference to report content and the URL narrows the scope.

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 provides no guidance on when to use this tool versus alternatives. There is no mention of prerequisites, context for typical use, or exclusions. It simply states the resource and parameters, leaving the agent to infer when this tool is appropriate from the name and URL alone.

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