local_gpu_confirm_mask

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 states that the tool confirms a mask but does not explain what confirmation entails—whether it validates, records, updates state, or rejects changed masks—nor does it describe side effects, failures, or output. This is a significant transparency gap.

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 a single, front-loaded sentence with no redundant wording. It efficiently conveys the core action and condition, earning every word's place without unnecessary elaboration.

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 tool's role in a workflow with siblings like local_gpu_prepare_mask and local_gpu_generate_round, the description lacks contextual completeness. It does not explain how this confirmation step fits into the overall process, what happens if the mask is not unchanged, or what the output represents. The presence of an output schema does not compensate for the missing workflow and behavioral context.

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%, and the description provides no information about run_id or mask_id. The phrase 'prepared mask' hints at mask_id but does not clarify relationships, expected values, or how the parameters interact, leaving the agent without adequate parameter understanding.

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 identifies the action (confirm), the object (an unchanged prepared mask), and the condition (after explicit overlay approval). It distinguishes this tool from siblings like local_gpu_prepare_mask and local_gpu_record_review by indicating a post-approval validation step, though it could be more explicit about the tool's unique role.

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?

Usage guidance is only implied through the phrase 'after explicit overlay approval,' which suggests a sequencing condition but does not explicitly state when to use this tool versus alternatives. No exclusions or alternative tools are mentioned, leaving the agent without clear decision-making criteria.

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