review

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

With no annotations provided, the description carries the full burden and does so thoroughly. It discloses persistence to state.json, automatic adjustment logic, reset semantics, direct parameter precedence over auto-adjustment, and mode-specific behaviors such as prewash ranges and lid settings.

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 long, but justifiably so for 11 parameters with substantial behavior semantics. It is front-loaded with the core purpose, followed by a well-organized parameter list where every line adds necessary decision and invocation 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 the high complexity, absent schema descriptions, and no annotations, the description covers side effects, defaults, precedence, resets, and all 11 parameter semantics. An output schema exists, so return-value documentation is not required. The only minor unstated edge case is behavior when a name fails fuzzy matching, but this does not undermine overall completeness.

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 must compensate, and it does comprehensively. Each parameter—name, feedback, mode, reset, g/temp/minutes, hours, prewash, lid, and note—is explained with meaning, defaults, valid ranges, and behavioral effects.

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 opens with a specific verb and resource: 「记录喝感评价，并自动微调该款茶的热泡/冷泡参数」, making it clear this is a review/refinement tool. It also distinguishes itself from siblings by emphasizing automatic parameter adjustment persisted to state.json, rather than just recording.

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?

The description clearly establishes when to use this tool: when actual drinking experience deviates from theoretical parameters due to merchant quality, aging, or storage issues. It provides detailed conditional behavior for different feedback words, but it does not explicitly name sibling alternatives or state when not to use it, so it stops short of a 5.

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