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

Annotations already declare openWorldHint=false, destructiveHint=false and readOnlyHint=false, so the description only needs to add context. It adds 'RETURNS: 若干候选选题' (several candidate topics), which is useful, but leaves unexplained why a pure idea-generation tool is marked readOnlyHint=false, whether anything is persisted, and roughly how many topics come back.

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 USE WHEN / ALIASES / RETURNS structure is scannable and front-loads the trigger. However, the opening 'Research topic generation / AI 生成研究选题' simply repeats the name and title verbatim, which is pure redundancy in an otherwise short description.

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 4-parameter generation tool with full schema coverage and no output schema, the definition covers trigger and rough return shape adequately. It is thin on what the returned candidates look like, how they are shaped by the required discipline versus optional fields, and whether any follow-up tool should be called next.

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 100%, so every parameter (discipline, specialty, keywords, research_type) is already documented in the schema and the baseline is 3. The description adds no extra meaning about how keywords or research_type influence the generated topics.

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 ('Research topic generation') and reinforces it with 'the user wants research topic ideas or directions for a field / clinical problem'. An agent knows exactly what it produces. It does not, however, distinguish itself from close siblings such as topic_gaps, topic_novelty, or topic_feasibility.

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 a clear trigger condition, and the ALIASES list broadens recognition of user phrasings in both Chinese and English. There is no explicit when-not guidance and no named alternative for related requests like gap analysis or feasibility, which keeps it below a 5.

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