get_us_stock_realtime_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. It mentions the return format ('Markdown格式' - Markdown format), which is useful. However, it lacks critical behavioral details: whether this is a read-only operation, potential rate limits, authentication needs, data freshness (e.g., delayed vs. real-time), error handling, or what happens with invalid codes. For a real-time data 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: the core purpose is stated first, followed by Args and Returns sections. Every sentence earns its place by clarifying the parameter and output format. Minor improvements could include bolding key terms, but it's efficient overall.

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 (real-time data fetching), no annotations, and an output schema (which handles return values), the description is minimally adequate. It covers the purpose, parameter meaning, and output format, but lacks behavioral context (e.g., rate limits, errors) and usage guidelines relative to siblings. With output schema existing, it doesn't need to detail return structure, but other gaps keep it from higher scores.

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 meaningful semantics for the single parameter 'code': it explains it's a US stock code with an example format ('us.AAPL' for Apple). Since schema description coverage is 0% (the schema only provides a generic 'Code' title with no details), this compensates well. However, it doesn't cover edge cases (e.g., invalid codes, formatting rules), preventing a perfect score.

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: '获取美股实时行情数据' (Get US stock real-time market data). It specifies the verb ('获取' - get) and resource ('美股实时行情数据' - US stock real-time market data), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_hk_stock_realtime_data' or 'get_historical_k_data', which would require a 5.

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 sibling tools like 'get_us_historical_k_data' for historical data, 'get_us_stock_basic_info' for static information, or 'get_us_market_indices' for broader market data. There's no context about prerequisites, timing, or limitations.

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