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

Annotations only declare idempotentHint and destructiveHint=false; they say nothing about persistence or cost. The description adds three things an agent could not infer: login is required, the call has real LLM cost, and it writes nothing to the database. It further sets expectations that category/tags/domain may come back null with OTHER as a normal fallback, which prevents the agent from retrying on a valid-but-empty result.

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?

Very dense and front-loaded with no filler; every clause (auth, cost, no-persist, null handling, workflow order, no-looping) earns its place. It loses a point for the run-on semicolon-chained formatting with no spacing, which makes it harder to scan than a short ordered list would be.

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?

There is no output schema, so the description must explain returns — it does so partially by naming the returned fields (category/tags/domain) and the OTHER fallback. Combined with auth, cost, and the follow-up workflow, an agent has enough to call it correctly, though the exact response shape remains implicit.

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% and already carries per-field hints, including the 'write it concretely for better classification' guidance for description. The description adds no syntax, format, or constraint information beyond the schema, so the baseline 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?

States a specific verb+resource ('草稿发布前LLM分类') and immediately scopes it as a trial that does not persist ('不写库'). It names the downstream siblings it feeds into (create_product / update_my_product), so an agent can place it in the workflow without opening another 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?

Gives an explicit ordering: classify → user verifies → create_product/update_my_product, and notes those siblings already auto-classify. It also adds a negative rule ('勿循环试探' – do not probe repeatedly), which is exactly the kind of when-not guidance most definitions omit.

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