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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds a return hint ('新颖性评估与相似研究') but says nothing about search scope, sources searched, or how thorough the novelty assessment is — notable for a literature-style check.

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?

The pipe-delimited structure front-loads purpose, then usage, aliases, and return value, with no redundant prose. The alias list is long but functional, serving as user-phrasing triggers rather than filler.

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?

For a one-parameter, read-only lookup tool with no output schema, the definition covers what it does, when to call it, and roughly what comes back. Only the search breadth and result format remain unspecified, which is a minor gap at this complexity.

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?

There is a single required parameter with 100% schema description coverage ('研究主题/题目'), so the schema already carries the semantics. The description adds no constraints on how specific or long the topic string should be, so baseline 3 applies.

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 definition states a specific check (novelty of a research topic) and elaborates it as 'whether a topic is new, or whether someone already published it', which clearly separates it from siblings like topic_gaps or topic_feasibility. It does not name those siblings explicitly, so differentiation is inferable 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?

The 'USE WHEN' clause gives an explicit triggering condition tied to user intent ('is this topic new / has someone published it'), which is directly actionable. It offers no when-not-to-use guidance or named alternative for overlapping questions such as gap analysis or feasibility.

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