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

With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently explains the side effects of both decisions: approving promotes the live capture to the new baseline, and rejecting fails the owning replay run. It also notes that the operation is owner-scoped. However, it does not mention reversibility, permission requirements, or what the response object contains, which are minor gaps for a mutation tool.

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, consisting of three sentences with no redundancy. It front-loads the core purpose, then clarifies the two decision outcomes, and finishes with a scoping note. Every sentence contributes value and the structure is logical.

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?

The description provides sufficient context for an agent to invoke the tool correctly: it explains the workflow (inspect via get_visual_review before resolving), the meaning of each decision, and the ownership scope. Since there is no output schema, the description does not need to describe return values, but it could mention the response format or confirmation details. Overall, it is nearly complete for this action-oriented tool.

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 all three parameters (review_id, decision, note) with adequate descriptions. The tool description does not add extra semantic detail beyond what the schema provides, so the baseline of 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 clearly states the tool's purpose with a specific verb ('Resolve') and a specific resource ('AI Visual Review candidate'), and distinguishes the two decision outcomes ('approve' and 'reject') with concrete consequences. It also references the prerequisite inspection step via get_visual_review, making its role unambiguous relative to sibling tools like list_visual_reviews and get_visual_review.

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 explicitly instructs the agent to inspect the candidate first via get_visual_review, establishing a clear workflow for when to call this tool. It explains the implications of each decision (promoting baseline vs. failing replay run) but does not mention when not to use it or alternative actions for unresolved cases. It gives strong context for appropriate usage.

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