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

Annotations already declare this a read-only, idempotent, non-open-world operation; the description adds genuine context beyond them: the glossary's size, partial-match semantics, and that the output is a translation in the requested language. It does not address multiple partial matches or case sensitivity, which keeps it below a 5.

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?

Two tight sentences with zero filler; the core behavior and the key constraint (English term, partial match) are front-loaded before the return-value detail.

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, the description carries the return-value burden and does so adequately by stating that a translation in the requested language is returned. Minor residual gaps: behavior on multiple matches and what happens when the requested language is not available.

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 baseline is 3, but the description adds real value: it enumerates the valid language codes (en, ko, ja, zh_tw, es, pt, id, vi) for a `lang` parameter whose schema has no enum, and clarifies `term` must be English despite partial matching.

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?

States a specific verb and resource (look up semiconductor terms in the VLSI multilingual glossary), quantifies scope (400+ terms), and pins down matching behavior (partial match on English term) and return (translation in requested language). Clearly distinguishable from the news/market/qna siblings.

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 describes the input expectation (English term, partial match) which implicitly guides usage, but gives no explicit when-to-use/when-not framing or routing to an alternative. Since no sibling is a plausible substitute, the omission is minor but still a gap.

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