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

With no annotations provided, the description carries the full load. It discloses the content of the returned payload (mix ratio, gear oil, plug gap, carburetor settings), which is useful context for a lookup, but says nothing about behavior on an unknown slug, whether the data is static reference material, or any rate/usage constraints.

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?

A single front-loaded sentence with the database tag up front and the substantive content following; no filler or repetition. Slight overhead from the bracketed category prefix.

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?

A simple single-parameter read tool with no output schema, so the bar is low, and the description does explain what data comes back. It remains incomplete on slug provenance and on what happens when the lookup misses, which an agent calling this blind would want to know.

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?

One required parameter (slug) with 0% schema description coverage, and the description never explains what a slug is or where it originates. For a lone required identifier that gates the call, this is a real gap the description should have closed.

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 pairs a specific verb (取得) with a clearly bounded resource (二行程車款的保養資料) and enumerates what that data covers (混合比、齒輪油、火星塞間隙、化油器設定). This distinguishes it reasonably well from sibling twostroke__get_model_specs, though the differentiation is implicit rather than stated.

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?

No guidance on when to call this versus twostroke__find_part or twostroke__get_model_specs, and no indication that the required slug presumably comes from search_models. The agent must infer both the trigger condition and where to obtain the identifier.

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