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

No annotations are provided, so the description carries the disclosure burden. It usefully characterizes the return type (signals + 信息差依据 / judgment layer, not raw data), which is genuinely helpful. But it omits auth needs, rate limits, latency, and whether answers are cached or time-sensitive—significant gaps for a tool calling an external inference layer with zero annotation coverage.

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 tight clauses, front-loaded with the core purpose and immediately followed by the differentiator. No filler, every sentence 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?

The description is adequate for a 2-param Q&A tool: it explains the answer shape and the distinction from data APIs. However, with no output schema and no annotations, an agent gets no information about response structure beyond a phrase, no error modes, and no guidance on question phrasing despite the schema's examples. Minimum-viable rather than 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 100%, so the schema already documents both parameters (question with examples, limit default 5). The description does not add syntax, format constraints, or meaning beyond what the schema provides, so the baseline of 3 applies.

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?

Names a specific verb+resource (natural-language question answering over industry questions) and adds an explicit differentiator: unlike the data APIs, it returns the judgment layer ('what it means') rather than raw data. This directly separates it from siblings like read_signal_board and read_article, which an agent can distinguish without opening any schema.

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 '区别于数据 API' clause tells the agent when this tool is the right choice—interpretive questions rather than raw data retrieval. No explicit when-not or alternative routing to a named sibling is given (e.g., no 'for raw signals use read_signal_board'), so it falls short of a 5 but the usage context is clear.

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