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 by structured data. The description adds only 'RETURNS: 预审意见', giving no sense of review depth, coverage areas, latency, or cost. With annotations carrying the safety burden, this is adequate but thin.

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?

The pipe-delimited USE WHEN / ALIASES / RETURNS layout is front-loaded and scannable, with the trigger condition appearing immediately. The 'RETURNS: 预审意见' segment is too vague to earn its place, but overall there is no padding.

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?

There is no output schema, so the description must explain the return value, and 'RETURNS: 预审意见' does not say whether the output is a risk list, severity ratings, section-by-section comments, or free prose. Input expectations (how much abstract/design detail to supply) are also unaddressed. For a review tool with four free-text inputs, this leaves an agent guessing about both inputs and outputs.

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% — all four fields (title, design, abstract, sample_size) carry descriptions, so the schema does the heavy lifting. The description contributes nothing about parameter meaning, e.g., whether abstract should be full text, how long, or how sample_size is used. 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 states a specific function: a pre-submission check that identifies likely rejection points, which is more than restating the name. It does not, however, distinguish this tool from similar siblings such as deep_peer_review, paper_six_agent_review, or acceptance_probability, so an agent cannot tell from the text alone which of these overlapping review tools to pick.

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 'USE WHEN' clause gives a clear triggering condition (user wants a pre-submission check for rejection risk), and the ALIASES list maps common user phrasings like '会被拒吗' and 'desk reject risk' onto this tool. There are no exclusions or named alternatives, which is a real gap given the crowded review-tool family.

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