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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so safety is covered. With no output schema, the description's '若干候选选题' is the only signal about return content, and it doesn't say how many topics, whether they cite literature, or whether the model is constrained to the given discipline.

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?

It is short, which is good, but it reads as a run-on concatenation of fragments with no punctuation or sentence boundaries, so purpose and usage blur together. Space is not wasted on repetition, but the lack of structure makes it harder to parse than a single well-formed sentence would be.

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 single-shot generation tool with no output schema, the description should convey what comes back and how constrained it is; '若干候选选题' covers the shape only superficially. It's adequate to attempt a call but leaves meaningful gaps about output quality, quantity, and grounding.

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%, and every parameter (discipline, specialty, keywords, research_type) is documented in the schema itself. The description only echoes the discipline idea via '给一个科室/疾病' and adds no meaning about how keywords or research_type shape 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?

The phrase 'AI 生成研究选题' names a verb and resource (generate research topics), and '若干候选选题' hints at the output. But it merely restates the title, and among the many topic siblings (act_topic_novelty, act_topic_feasibility, act_topic_gaps, act_idea_ignite, act_topic_research_plan) nothing distinguishes this tool from them.

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?

'用户想找选题、不知道做什么方向、给一个科室/疾病要几个可做的题目' does give a recognizable trigger scenario for the agent. However, it never says when NOT to use it or which sibling to prefer for feasibility, novelty, gap analysis, or a full research plan.

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