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

Annotations already cover read-only, idempotent and non-destructive status, but the description adds real behavioral context the annotations cannot: no price lookup, no quota consumption, no acquisition initiated, and deterministic plain arithmetic. The remaining gap is that it never says what the result looks like or in what unit it is returned, though the integer-fen convention for inputs makes that largely inferable.

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 precondition and the required-input rule come first, followed by per-parameter caveats and then a boundary statement, so the most decision-relevant information is front-loaded. The text is dense and largely waste-free, though the asking_fen and resale_drop_fen sentences repeat the 'only when the user explicitly states it' pattern and could be tightened.

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 five-parameter arithmetic tool with no output schema, the description supplies units, requiredness, preconditions, optional-parameter restrictions and scope boundaries, which is close to everything needed to call it correctly. The only missing piece is how asking_fen and resale_drop_fen affect the computed ceiling, which would let an agent predict the result rather than just supply inputs.

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 0%, so the description carries the burden and does so for all five fields: it fixes the unit ('所有参数为整数分'), marks the three money inputs as mandatory, and constrains the two optional ones (asking_fen is the user-selected quote only; resale_drop_fen is a user-stated resale reduction, not a default new-car pass-through). It stops short of stating how asking_fen and resale_drop_fen enter the formula, which is the one semantic detail a caller might still want.

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 opening sentence gives a concrete verb and output (测算收车上限) plus the three inputs that must be present (预期实际卖价、总成本、目标利润), so the agent knows exactly what this computes. The closing line '此工具只做条件算术，不查价、不扣次数、不发起收购' further separates it from pricing-lookup siblings like compare_quote, search_quotes and get_market_card.

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 names a gating condition ('车商明确提供…时'), an alternative action when inputs are missing ('缺项先询问'), and explicit exclusions for two optional parameters (asking_fen only when the user explicitly selected a quote; resale_drop_fen only when the user specified a drop, with no assumption that new-car discounts pass through proportionally). These are concrete when-to-use and when-not-to-use rules rather than implied guidance.

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