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

Annotations already declare this a safe, closed-world read, and the description adds two things annotations cannot: the return payload ('中稿概率与依据' – a probability plus supporting rationale) and a workflow prerequisite hop via journal_id. With no output schema, disclosing the return shape is genuinely useful. It does not discuss confidence intervals, model caveats, or what happens with a sparse abstract, so it falls short of full behavioral disclosure.

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 KEY: value structure front-loads the action, then the trigger, aliases, and return value, so an agent can scan it quickly. Every segment is functional. The only redundancy is that the bilingual title repeats the tool name already given as NAME, which costs a few tokens without information.

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 2-parameter read tool with a nested paper object, the definition covers the trigger, the aliases, the required inputs (via schema), and the return content. Missing pieces are modest: no statement about how much of the paper is needed for a meaningful prediction, and no caveat about the inherent uncertainty of the estimate. Given the simple schema and read-only annotations, an agent has enough to call it correctly.

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%, with both paper (nested, title/abstract) and journal_id documented including the hint that journal_id can come from a journal-recommendation action. The description repeats the requiredness of both parameters and the journal-scoping intent, adding marginal but consistent reinforcement rather than new semantics. Baseline for full coverage is 3, and the added workflow note in the description text nudges it slightly higher.

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 definition names a specific resource and outcome – the probability of acceptance at a given journal – and the 'USE WHEN' line reinforces that this is a prediction, not a search or recommendation. It is clearly distinguishable from the listed siblings, which are unrelated statistical/formatting utilities. What is slightly missing is an explicit verb framing (e.g., 'predicts the acceptance chance'), since the title alone reads like a metric name.

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 concrete trigger: the user asks the chance of acceptance at a specific journal, and the alias list (中稿概率, 能中吗, 投中的机会) helps match colloquial phrasings. There is no when-not guidance and no named alternative, but the sibling set contains no obvious competing acceptance-prediction tool, so the risk of misrouting is low.

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