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

Annotations only declare idempotentHint and destructiveHint=false, so the description carries most of the burden and does add real context: the text is 公开可见 (publicly visible), evaluation uses an expensive real LLM, identical text is deduped, and declaredContext can still be inferred as source. It also maps three failure codes to remedies. It does not state permission requirements beyond 登录 or what happens to a previously set position.

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?

Zero filler words and the login precondition is front-loaded, but the telegraphic semicolon-chained fragments and bare error-code tokens (position_unclear, chain_source_changed, llm_unavailable) make it hard to parse on first read. Density here trades against scannability; a short structured breakdown of the failure codes would have been clearer.

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?

With no output schema and only minimal annotations, the description steps up: it covers visibility of the written text, LLM cost/dedup behavior, the follow-up read tool, and recovery paths for three error states. Only the auth/permission model and the effect of overwriting an existing anchor are left 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%, so describe's content rules (5–500 chars, whom/what/how) are already documented in the schema and the description largely restates them. It adds one genuinely new pointer — that product ids come from get_my_products — which helps with subjectId/subjectType. Baseline 3 is appropriate given the schema does the heavy lifting.

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 action (归位 — placing yourself or a product at a position in the industry chain) and explains what the core input should contain (服务谁、提供什么、怎样交付). The concept of 产业链归位 is jargon-heavy but the describe clause makes it concrete. It does not explicitly contrast with nearby siblings such as get_my_positioning or list_my_chain_anchors, so it stops short of 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?

Gives a clear call sequence: log in, confirm, then position yourself or a product referenced from get_my_products, and follow up with get_chain_anchor. It also tells the agent when NOT to act (position_unclear, relations_unclear, 均勿盲重试) and bounds retries. What it lacks is guidance on when to use this versus other self-profile tools like set_my_role_profile or update_my_profile.

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