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

The description adds substantial context beyond the annotations: it requires login, incurs real LLM cost, does not write to the DB, and may return null category/tags/domain with OTHER as a normal fallback. That is meaningful operational context an agent could not derive from the schema. Note a mild tension: it says '不写库' (no DB write) while readOnlyHint=false implies a non-read-only operation; this reads as conservative cost-based marking rather than a true contradiction.

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 【需要登录】/【组合链】bracketed structure front-loads the login precondition and then the workflow, so the highest-priority facts come first. It is dense and jargon-heavy but nearly every clause carries distinct information (auth, cost, no-write, null/fallback, chain, anti-loop).

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?

For a 3-parameter tool with no output schema and rich annotations, the description supplies the missing pieces: auth requirement, cost, write behavior, null/fallback output semantics, and the surrounding workflow. An agent has enough to call it correctly and to know when not to repeat 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?

With 100% schema description coverage, the schema already documents name, tagline, and description including defaults and length limits. The description adds no input-syntax detail; its 'category/tags/domain可null' remark concerns outputs, not inputs. Baseline 3 applies when the schema carries the parameter burden.

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 verb+resource: an LLM-based classification of a product draft before publishing ('草稿发布前LLM分类'). It also distinguishes itself from siblings by noting that create_product/update_my_product perform their own auto-classification, so the agent knows this is the preview-only step. It is slightly cryptic due to terse abbreviations, but the purpose is unmistakable.

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 an explicit workflow: classify → user verifies → create_product/update_my_product, and clarifies that the downstream tools auto-classify, which tells the agent when this call is redundant. The '勿循环试探' (don't loop-probe) warning further constrains usage. It stops short of stating an explicit when-not-to-use condition, keeping it from a 5.

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