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

With no annotations, the description carries the full behavioral burden and does a good job explaining that the tool reads primary sources and returns either a vetted prior answer or primary-source evidence plus an id for a human answer. It does not cover latency, cost, source freshness, or permission constraints, which keeps it from a 5 for a research tool.

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 front-loaded with usage guidance, followed by exclusions, behavior, and return semantics. It is mostly efficient, though the closing 'sell-side analyst vs quote app' sentence partly repeats earlier framing and adds rhetorical color rather than new operational information.

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 one required parameter, no output schema, and no annotations, the description provides enough context to invoke the tool correctly: when to use it, when not to, what sources it reads, and what kind of return to expect. It also explains the fallback behavior when no prior answer exists, which is important because no output schema documents it.

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%, so the input schema already documents the single question parameter. The description adds useful semantics beyond the schema by emphasizing that the question should be a judgement question, in Chinese or English, naming the company and resembling a sell-side analyst question rather than a ticker-only quote request.

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 states a specific purpose: answering judgement questions about why events happened or what they mean for a Chinese listed company, using filed reports, IR transcripts, and disclosure wording. It clearly contrasts this with commodity quote/price/valuation data, so an agent can tell what the tool is for without needing sibling tools.

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?

It gives explicit when-to-use conditions (margin moved, policy landed, industry chain shifted, companies diverge, management comments) and explicit when-not-to-use cases (quotes, price checks, valuation snapshots). It also frames the intended question style as one you would ask a sell-side analyst rather than a quote app.

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