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

Annotations already declare readOnlyHint, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds genuinely new behavioral context: the output is item-by-item (逐条核对结果) and is produced by a real algorithm engine rather than LLM generation, which tells the agent the result is deterministic and citable. It does not describe how many guidelines are supported or how results are structured.

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 — operation label, usage trigger, return value — with no filler, and the trigger is front-loaded. The bracketed label partially repeats the annotation title, which is the only redundancy, but it costs almost nothing.

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 two-parameter, read-only check with no output schema, the description covers what goes in (a paper) and roughly what comes out (per-item results). Gaps remain: which reporting guidelines are actually supported, and what role design_type plays in selecting a guideline. Adequate but not complete.

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 both parameters (text = 论文正文, design_type = 研究设计类型) are already documented in the schema. The description adds no extra meaning about the expected format of text, valid design_type values, or why design_type matters for guideline selection. Baseline 3 applies when the schema carries the parameter burden.

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 operation (核对/check) against a specific resource (报告规范清单, e.g. CONSORT/STROBE), plus a concrete trigger scenario (user asks whether a paper complies). This clearly separates it from analysis siblings like act_consort_flow, which produces a flow diagram rather than a compliance checklist, though no sibling is named explicitly.

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 '用户问论文符不符合 CONSORT/STROBE 等报告规范' is an explicit when-to-use trigger tied to a user intent, which is stronger than most siblings get. It stops short of stating when NOT to use it or which sibling to pick instead (e.g. act_consort_flow vs this tool), so it is not a full 5.

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