美股盈利数据

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

With no annotations, the description partially discloses behavioral traits: it mentions data source fallback (Alpha Vantage with API key to yfinance free). However, it does not discuss rate limits, error handling, data freshness, or whether the data is for a specific time range. The disclosure is helpful but incomplete.

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?

Two concise sentences, front-loaded with purpose, followed by technical detail. No unnecessary words; every sentence adds value.

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 simplicity (one parameter, no output schema), the description covers purpose and data source but fails to explain the output format or expected return values. An agent would not know what data fields to expect, which is a gap in completeness.

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 100% with a clear description of the 'symbol' parameter. The description adds context about what data is fetched and data source behavior, but does not provide additional meaning for the parameter beyond stating it's a US stock symbol. Baseline 3 is appropriate.

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 retrieves historical earnings data and analyst expectations for US stocks, with specific verb '获取' and resource '美股历史盈利数据和分析师预期'. It differentiates from siblings by mentioning historical earnings and analyst expectations, though it doesn't explicitly contrast with similar tools like stock_earnings_calendar.

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?

No guidance on when to use this tool versus alternatives like stock_earnings_calendar or stock_financials_us. The description lacks context on prerequisites, such as requiring an API key for Alpha Vantage, and does not specify typical use cases or limitations.

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