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

No annotations are provided, so the description carries the burden. It usefully discloses the return content (結論、比較表、適用情境), which is genuine behavioral context for an informational tool, but it says nothing about permissions, rate limits, or how the id/term lookup resolves.

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 compact sentences, front-loaded with a bracketed domain label and then the action, with zero filler. Efficient, though the bracketed tag and parenthetical examples are slightly dense.

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 and no annotations, the description does cover the return shape and the two input modes, which is the essential information. However, the meaning of the optional 'id' parameter and the choice against neighboring glossary tools are left unresolved, so it is adequate but not fully complete.

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 schema adds no parameter meaning. The description partially compensates by explaining the two input modes ('可給 id，或 a、b 兩個名詞'), clarifying the either/or relationship between id and a/b, but it never defines what an 'id' refers to.

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 names a specific verb (比較／compare) and resource (兩個 AI 概念／two AI concepts), plus concrete pairs (RAG vs 微調, MCP vs API, SEO vs GEO) that make the scope unmistakable. This inherently distinguishes it from glossary__explain_term, which handles a single term, so an agent can pick correctly without opening the schema.

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 examples imply when this tool is appropriate (pairwise concept comparison), which is useful, but there is no explicit statement of when to use it versus glossary__explain_term or glossary__search_glossary, and no when-not guidance. Usage is implied rather than directed.

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