stock_hk_daily

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the read-only and idempotent nature is pre-established. The description adds practical behavioral context by explaining the adjust parameter (unadjusted, forward-adjusted, adjustment factor) and the return type (pandas.DataFrame). It does not disclose rate limits, date range behavior, pagination, or specific output columns, but given the annotations cover the safety profile, this is adequate.

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 well-structured docstring with a title, source URL, parameter definitions, and return type. It is front-loaded with the purpose and each section is informative. The final return line '指定 adjust 的数据' is slightly redundant, but overall the text is compact and earns its place.

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?

For a simple two-parameter read-only tool, the description covers the source, parameter semantics, and return type. However, it omits details about default date ranges, whether the full history is returned, the structure of the DataFrame (column names), and any limitations. Since there is no output schema to provide this information, the description could be more complete.

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 description coverage is 0%, so the description carries the full burden of explaining parameters. It clearly defines the 'adjust' parameter with three valid values ("", qfq, qfq-factor) and notes that 'symbol' can be obtained from another function. It does not specify the exact symbol format beyond a default value and URL example, but this is sufficient for basic usage.

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 states '新浪财经-港股-个股的历史行情数据' (Sina Finance HK stock individual historical market data), which clearly identifies the resource and the retrieval action. However, it does not explicitly mention 'daily' in the text (implied by the tool name) and does not differentiate from sibling tools like stock_hk_hist or stock_hk_hist_min_em, so it is clear but lacks sibling distinction.

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?

There is no guidance on when to use this tool over alternatives. The only usage-related hint is that 'symbol' can be obtained via ak.stock_hk_spot(), which addresses parameter sourcing rather than tool selection. No exclusions or preferred scenarios are provided, leaving the agent without context for choosing this tool among many similar stock history tools.

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