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

With no annotations provided, the description carries the full burden, and it partially meets it by disclosing the return content (結論、比較表、適用情境). It says nothing about side effects, permissions, or rate limits, though the tool is evidently a read/query operation.

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 concrete examples and then output/parameter behavior, with no filler. Well-sized for a simple lookup tool.

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?

No output schema or annotations exist, so the description must stand alone; it covers output shape and input modes but omits the meaning of 'id' and any failure/edge behavior, leaving modest gaps for a 3-param tool.

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 0% and the parameters are bare strings, so the description must compensate. It does add meaning by explaining the two input modes (id or a/b pair), but leaves 'id' undefined (an id of what?) and does not clarify whether a/b are names or slugs.

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 (compare two AI concepts) and grounds it with concrete examples (RAG vs 微調, MCP vs API). It implicitly distinguishes itself from siblings like explain_term (single-term explanation) by focusing on pairwise comparison, though it never names those alternatives.

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 the tool is useful (comparing two concepts), and the last sentence clarifies it accepts either an id or two terms. However, there is no explicit when-to-use versus when-not guidance or routing to siblings such as explain_term for single concepts.

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