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 the return artifact ("RETURNS: 论文段落"), which is genuine added value since there is no output schema. However, "with their own instrument settings" implies the caller supplies instrument parameters, and the schema contains no such parameter (only software, normalization, experiment_type), so the behavioral claim is not consistent with the actual interface.

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 compact and the USE WHEN condition is front-loaded, but the title is duplicated in the description and the ALIASES block is keyword padding rather than information an agent needs to invoke the tool. The mixed-language fragments (RETURNS: 论文段落) reduce polish.

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 generation tool with no output schema, the description correctly states the return artifact and the use condition. It omits what happens with zero arguments (all parameters are optional), what depth/length the paragraph will have, and how this differs from omics_methods_draft — gaps that matter for a drafting 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?

Schema coverage is 100%, so all three optional parameters are documented in the schema itself, making 3 the baseline. The description's LFQ/TMT/DIA and MaxQuant/DIA-NN mentions loosely hint at experiment_type and software values, but add no syntax, defaults, or constraints beyond the schema.

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 resource and modality — a proteomics methods section covering LFQ/TMT/DIA — so an agent knows exactly what artifact is produced. It does not explicitly differentiate itself from the sibling omics_methods_draft, which is the nearest overlap, so it falls short of a 5.

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?

"USE WHEN: the user wants a proteomics ... methods section with their own instrument settings" gives a clear triggering condition. There are no exclusions and no named alternative for the adjacent omics case, so this is clear context without routing guidance.

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