review_paper

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

No annotations are provided, so the description carries the full burden. It discloses that the tool is advisory, that `confidence` should be checked, and that the venue hint 'shapes expectations only' (i.e., it does not guarantee venue-specific behavior). It does not disclose details like rate limits, failure modes, or how confidence is computed, but the core behavioral caveat is clearly stated.

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 sentences with no filler. The first sentence front-loads the tool's purpose and outputs; the second adds the critical advisory caveat. Every clause earns its place.

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 tool with 100% schema coverage and no output schema, the description covers the input, the output dimensions, and the key behavioral caveat. It could mention what happens with malformed or empty input, but nothing essential for a correct call is missing.

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 schema already documents both parameters. The description adds context for `venue` ('shapes expectations only') and clarifies that `text` can be any passage, but it does not add substantial meaning beyond the schema. Baseline 3 is appropriate.

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 verb ('returns calibrated ratings'), a clear resource ('Academic paper-review scorer'), and the exact outputs (soundness/novelty/clarity ratings, verdict, recommendation). It distinguishes itself from generic text-processing tools by naming the decision model (Jev/System One) and the ordinal scales. No sibling tools exist, so no sibling differentiation is needed.

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 description implies when to use it: given a paper excerpt, it returns ratings and a verdict. It also gives a clear usage caveat: treat the verdict as advisory and check `confidence` before acting. It does not explicitly state when not to use it or name alternatives, but with no siblings and a clear input type, the context is sufficient.

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