get_stock_daily_trading

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

No annotations are provided, so the description bears full responsibility. It thoroughly discloses the return envelope (success, data, error, tool), enumerates the data fields within data, and lists realistic error causes (nonexistent symbol, non-trading day, source unavailable). It does not cover read-only guarantees, authentication, or rate limits, but the return and error behavior is well specified for a GET-style data query.

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 organized into clear sections (overview, examples, Args, Returns, Raises) with the primary purpose front-loaded. The first two sentences are somewhat redundant ('取得股票日交易統計資訊' vs '提供指定股票當日的詳細交易統計'), but overall the length is justified by the detailed return-field enumeration and error logging.

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 single-parameter tool with no output schema, the description provides everything an agent needs to invoke it correctly: parameter meaning, example calls, full return structure and field names, and likely failure scenarios. There is no missing critical information.

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 input schema merely defines symbol as a required string with no description (0% coverage). The description compensates fully by defining the parameter as '股票代碼' (stock symbol) and providing two concrete examples ('2330', '0050'). This leaves no ambiguity about what value to supply.

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 opens with an explicit verb and resource: '取得股票日交易統計資訊' (retrieve stock daily trading statistics). It then details the data covered (成交量, 成交金額, 委買委賣資訊) and gives concrete examples. The '日' (daily) qualifier distinguishes it from sibling tools like get_stock_monthly_trading, get_stock_yearly_trading, and get_real_time_trading_stats.

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?

Usage is implied through examples and the phrase '當日' (current day), which signals this tool is for daily end-of-day trading statistics. However, the description does not explicitly state when to prefer this tool over alternatives or mention any exclusion conditions, so the agent must infer routing from sibling names and context.

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