get_hk_dupont_data

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return format ('Markdown格式的杜邦分析数据表格或错误信息' - Markdown-formatted DuPont analysis data table or error message), which adds some value. However, it lacks critical behavioral details such as whether this is a read-only operation, potential rate limits, authentication needs, data freshness, or error handling specifics. For a data retrieval tool with zero annotation coverage, this is insufficient.

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 appropriately sized and front-loaded. It starts with the core purpose, followed by a structured 'Args' and 'Returns' section with bullet points. Every sentence earns its place by defining parameters and output format without redundancy or fluff, making it efficient 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?

Given the tool's moderate complexity (3 parameters, no annotations, but has an output schema), the description is partially complete. It covers parameter semantics well and mentions the return format, but lacks behavioral context (e.g., read-only status, error conditions). The output schema existence means the description doesn't need to detail return values, but overall gaps in usage guidelines and transparency keep it at a baseline adequacy level.

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 description adds significant meaning beyond the input schema, which has 0% description coverage. It explains each parameter: 'code' as '港股代码 (如 'hk.00700')' (Hong Kong stock code, e.g., 'hk.00700'), 'year' as '4位年份 (如 '2023')' (4-digit year, e.g., '2023'), and 'quarter' as '季度 (1, 2, 3, 或 4)' (quarter: 1, 2, 3, or 4). This compensates fully for the schema's lack of descriptions, providing clear semantics and examples.

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 states the tool's purpose: '获取港股季度杜邦分析数据 (ROE分解)' translates to 'Get Hong Kong stock quarterly DuPont analysis data (ROE decomposition)'. It specifies the verb ('get'), resource ('Hong Kong stock DuPont analysis data'), and scope ('quarterly'), which distinguishes it from siblings like 'get_dupont_data' (likely general) or 'get_hk_stock_analysis' (broader). However, it doesn't explicitly differentiate from all siblings, such as 'get_hk_profit_data', which might overlap in financial data retrieval.

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. It doesn't mention prerequisites, exclusions, or comparisons to sibling tools like 'get_dupont_data' (for non-HK stocks) or 'get_hk_stock_analysis' (for broader analysis). Usage is implied by the parameters but not explicitly stated, leaving the agent to infer context from the tool name and parameters alone.

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