tag_video_face

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 mentions that the cropped frame is indexed into Rekognition, which implies persistence and side effects, but it does not explain whether this operation is reversible, what permissions are needed, or any failure modes. The description is too sparse to fully inform the agent of the tool's behavioral traits.

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 front-loads the primary action ('Manually tag a face track in a video') and then adds a relevant detail (indexing into Rekognition). Every word earns its place, and there is no redundancy or fluff, making it highly 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?

For a mutation tool with 4 parameters and no output schema or annotations, the description is incomplete. It does not explain the return value, whether the operation is idempotent, how duplicate or existing tags are handled, or any prerequisites beyond the person_index. The mention of Rekognition indexing adds some context, but significant gaps remain for an agent to use the tool correctly.

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?

The input schema provides 100% coverage of parameter descriptions, so the baseline is 3. The description does not add meaningful information beyond the schema—it only restates that tagging is manual, which does not clarify parameter semantics further. The person_index parameter is already documented in the schema as coming from face detection.

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 ('manually tag a face track in a video') and identifies the resource (face track in a video). It also mentions indexing into Rekognition, adding clarity on what the tagging accomplishes. However, it does not explicitly distinguish itself from the sibling tool 'tag_asset_face', though 'video' and 'face track' provide some differentiation.

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?

No guidance is provided on when to use this tool versus alternatives. There are no explicit when-to-use or when-not-to-use instructions, nor are any alternative tools mentioned. The usage context is only implied by the description's wording, which is insufficient for an agent to decide between this and similar face-tagging tools.

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