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

Annotations already declare readOnlyHint=true, openWorldHint=false and destructiveHint=false, so the safety profile is covered. The description adds a genuinely useful behavioral claim — '真实算法引擎执行，非大模型生成' — telling the agent results are deterministic engine output rather than LLM-composed. It does not cover runtime, input size limits, or what the association results actually contain.

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 compact sentences with the resource tag front-loaded and the return value explicitly flagged with 返回：. Every clause carries information; the only mild redundancy is restating the three data types that also appear in the schema.

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 four-parameter, all-optional analysis tool with full schema coverage and no output schema, the definition is reasonably complete: it names inputs, purpose, and the nature of the output. The notable gap is that no parameters are required, yet the description never says what the tool does when some or all omics layers are omitted.

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 the four parameters (mRNA, protein, metabolite, pathway_map) are already documented in the schema. The description echoes the three data types but adds nothing about the format of these lists, what pathway_map values are acceptable, or what happens with only partial inputs. 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 gives a specific verb and resource: integrating mRNA/protein/metabolite data together and returning multi-omics association results. The bracketed tag 【多组学整合】 reinforces the resource. It does not name any sibling it should be chosen over (e.g. act_enrich, act_metabo_pca, act_protein_enrichment), so differentiation relies on the agent inferring from the resource names alone.

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 phrase '用户要把 mRNA/蛋白/代谢物一起整合分析' implies the trigger condition — use this when all three omics layers must be analyzed jointly — but there is no explicit when-not guidance and no mention of alternatives such as single-omics enrichment tools in the sibling list. Usage is inferable rather than stated.

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