index_analysis_daily_sw

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, so the safety profile is covered. The description adds the source URL and the return type (pandas.DataFrame), but it does not disclose what the returned data actually contains, any potential data freshness issues, or other behavioral details. This is modest added value 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 a compact docstring with clear sections for source URL and parameters. It is well-structured and not overly verbose. The only redundancy is repeating the title as the first line, but that is minor. Overall, it is concise and easy to parse.

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?

This is a simple read-only tool with three optional parameters and no output schema. The description defines all parameters and provides the source URL, but the return value is only described as '指数分析', which is vague. Since there is no output schema, the description should better explain what columns or data the DataFrame contains. The tool is not complex, but the missing output detail leaves a gap.

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 no descriptions (coverage 0%), so the description must compensate. It provides the allowed values for symbol (市场表征, 一级行业, 二级行业, 风格指数) and explains start_date/end_date as 开始日期/结束日期. This adds meaning beyond the bare schema. However, the date format is not explicitly given (only implied by defaults), so it is slightly incomplete.

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 resource (指数分析 from 申万宏源研究) and the scope (choice of symbol categories). The return type is stated as pandas.DataFrame. However, the verb (e.g., 'retrieve', 'fetch') is not explicit, and sibling differentiation (daily vs. weekly/monthly) relies on the tool name rather than the description.

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 like index_analysis_weekly_sw or index_analysis_monthly_sw. There are no use cases, prerequisites, or exclusions. The docstring only lists parameters without contextual usage direction.

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