vlm_review

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

With no annotations provided, the description carries the full burden of disclosing behavioral traits. It does not state whether the tool mutates anything, whether it requires network access for a model, how long it might take, or what it returns (e.g., a score, annotations, a report). 'Semantic review' implies analysis but not the consequences or side effects, such as whether it modifies the project or only reads the video.

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, concise sentence that immediately communicates the action and the target resource. It is front-loaded with the verb and object. However, its brevity may be a result of under-specification rather than deliberate conciseness, but as written it's efficient.

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 complexity (3 parameters, no output schema, no annotations), the description is incomplete. It does not explain the parameters, expected output, or any behavioral context. An agent would struggle to invoke it correctly, especially without knowing what 'brief' or 'model' are for. It fails to provide sufficient guidance for a semantic review 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 0%, so the description must compensate. It does not explain any of the parameters (video_path, model, brief). The agent cannot infer what 'model' or 'brief' mean from the description alone. For example, it doesn't clarify that 'brief' might be instructions for the review or that 'model' selects the VLM. This is a significant gap.

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 action on a resource: it runs a VLM semantic review on a rendered video, distinguishing it from basic QC or rendering siblings. However, it is somewhat terse and doesn't detail what the review entails or what kind of output or decision it produces, leaving some ambiguity for an agent.

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 gives no explicit guidance on when to use this tool versus siblings. It could be inferred from the name that it's for evaluating video content, but there's no indication of prerequisites (e.g., video must be rendered) or contexts where it should be preferred over QC or other analysis tools. The mention of 'rendered' hints at a post-render step, but it's not explicit.

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