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

No annotations are provided, so the description carries the full behavioral burden. It does disclose the response composition (definition, how it works, examples, misconceptions, FAQ, original sources), which is genuinely useful since no output schema exists, but it says nothing about handling unknown terms, sourcing/grounding limits, or response length.

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 sentences, front-loaded with the verb and resource before the details. Acceptably sized and free of filler; the example list is a bit long but earns its place by disambiguating the term scope.

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 one-parameter lookup with no output schema, and the description does explain what the response contains and what input forms are accepted. Only the lack of routing guidance relative to sibling tools keeps it short of 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 coverage is 100% for the single 'term' parameter, so the baseline is 3. The description adds meaning beyond the schema by stating that Chinese, English, and aliases are all accepted, clarifying the matching semantics the schema does not express.

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 (解釋 / explain) and resource (AI 或科技名詞), with concrete examples (MCP, RAG, AI 代理人, 幻覺, GEO, x402) and an enumeration of what the answer contains. The purpose is unmistakable, though it never explicitly distinguishes itself from siblings like search_glossary or compare_concepts, which could plausibly overlap.

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?

There is no when-to-use guidance and no mention of alternatives. With siblings such as search_glossary, compare_concepts, and get_howto present, the agent must guess whether this tool or a sibling is the right entry point; the language note (中文/英文/別名) is a matching detail, not usage guidance.

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