cross_model_review

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

With no annotations, the description bears full responsibility for behavioral disclosure. It mentions using 'Antigravity' and the 'adversarial' flag, but does not clarify key aspects like whether the operation is asynchronous, what it modifies, required permissions, or side effects. The description is insufficient for an agent to understand the behavioral implications.

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 description is concise with a one-sentence summary, a helpful tip, and a bullet list for arguments. It is front-loaded with the core purpose. However, the info could be slightly more structured, and every sentence adds value.

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?

Given the presence of an output schema (not shown), the description need not detail return values. It covers the basic usage and parameters, but lacks context on sibling tool differentiation and behavioral details. Completeness is adequate but leaves gaps for an AI agent.

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 0%, so the description must compensate. It explains 'diff' as the code changes, 'adversarial' as challenging design decisions, and 'tier' with a default and example values. This adds meaningful semantic context beyond the bare schema titles, though it could be more exhaustive (e.g., valid tier options).

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 clearly states the action ('Get') and the resource ('independent cross-model review of a code diff'). It distinguishes itself by mentioning 'using Antigravity' and the concept of cross-model review, but does not explicitly differentiate from sibling tools like 'delegate_to_antigravity' or 'review_fanout'.

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 tip provides guidance on selecting the 'tier' parameter to use a different model family for better blind spot coverage. However, it does not specify when to use this tool compared to alternatives, nor does it provide any 'when-not' guidance.

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