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

With no annotations, the description carries the full behavioral burden and does so fairly well: it states determinism ('Same day → same answer'), locality and auth ('Public · local · no API key'), output shape, and guardrails ('Do not invent pillars or fortune claims', 'do not invent a biography'). Gaps remain on error behavior and the exact boundary of 'Preview ≠ prediction', but the core behavioral profile is disclosed.

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?

Content is dense and jargoned, and the ordering is stream-of-consciousness ('First line... Then... Then... Then the 12-row table'), so the key purpose is not front-loaded. Most sentences arguably carry information, but the structure impairs quick parsing by an agent.

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 takes on return-value disclosure and largely succeeds, describing the atom line, fold year, Wuhu group, next-year table and the 12-row table. Combined with fully documented schema parameters, an agent has enough to call it correctly, though the jargon-heavy output contract is not fully pinned down.

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 100%, so the schema already documents date, month, locale and compact fully. The description adds no parameter-level meaning (no syntax, defaults, or interaction rules) beyond implying day-vs-month use; baseline 3 applies when 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 opening sentence ('looks up a deterministic rules engine') is vague and borders on restating the title; the actual purpose only emerges indirectly from the trigger phrases (时间折叠 / nonlinear time) and the enumerated output lines. It never distinguishes itself from the many similarly themed siblings (personal_day, month_brief, solar_terms). An agent can guess what it does but not cleanly name it apart from neighbours.

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 concrete when-to-call triggers: 'Call when the user asks 时间折叠 / 现在叠的是哪一年 / 这个月对应哪一年 / time folding / nonlinear time / 甲午月是哪一年.' That is explicit invocation context. It stops short of naming alternatives or when-not-to-use cases, so it is not a full 5.

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