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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds one genuinely novel behavioral claim: results come from a real algorithm engine rather than LLM generation, which matters for trust. It says nothing about data sources, coverage, or what happens when a gene name is not recognized.

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?

Three compact segments: a bracketed label, the trigger condition, and the return type plus provenance note — all front-loaded with zero filler. The bracketed label largely restates the annotation title (基因信息检索), which is minor duplication but not bloat.

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?

This is a simple one-parameter read tool with annotations and no output schema, so the description's coverage of purpose, trigger, and return kind is close to sufficient. It could be stronger by mentioning the underlying gene database or behavior for ambiguous gene symbols, which would matter for a lookup 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?

There is a single required parameter with 100% schema description coverage ('基因名/关键词'), so the schema already carries the semantics. The description's mention of aliases and function hints at what the query string can contain, but adds no format, spelling, or organism-scope guidance beyond the schema. 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 description names a specific verb (检索/retrieval) and resource (基因信息), and even enumerates the question types it answers: what a gene does, its aliases, and its function. It does not distinguish itself from near-neighbors such as act_gene_pathway, act_enrich, or act_lit_search, so an agent still has to infer the boundary.

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?

It provides a clear activation condition — '用户问某个基因是干什么的、别名、功能' — which reads like a routing rule an agent can match against user intent. There is no explicit when-not guidance and no named alternative (e.g., use act_gene_pathway for pathway membership), so the selection boundary remains implicit.

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