美股公司概览

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

Without annotations, the description carries full behavioral disclosure burden. It adds value by explaining data source fallback behavior and API key requirement. However, it omits details like rate limits, caching, or error handling. This is above average but not complete.

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 single efficient sentence, front-loaded with purpose, followed by data points and data sources. No redundant words; every clause 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?

The description covers purpose, data points, and data sources. No output schema is provided, but the description lists expected outputs. It lacks mention of return format or pagination, but the tool is simple (single param). Minor gap prevents a 5.

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 description coverage is 100% (single parameter with clear description). The tool description does not add extra semantics to the parameter itself; it only contextualizes the output. Baseline score of 3 is appropriate as the description does not degrade or enhance parameter understanding.

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: '获取美股公司基本面概览' (get US stock company fundamental overview) and lists specific data points (market cap, PE, EPS, dividend yield, 52-week high/low, analyst ratings). This distinguishes it from sibling tools like stock_financials_us or stock_info, which focus on detailed financials or general info.

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 mentions data source fallback (Alpha Vantage with API key, then yfinance free), but does not explicitly state when to use this tool versus other stock sibling tools. Usage context is implied but not clearly articulated with when-not or alternative recommendations.

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