label_video_deepfake

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

There are no annotations, so the description carries the full burden. It explains the output (label text, placement guidance, compliance info) and notes that artistic/satirical works 'may qualify for exemption'. However, it does not disclose potential side effects, authentication requirements, or how exemption is determined, and it does not state that the tool only uses a description (not the actual video) beyond the arg description.

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 reasonably concise and front-loaded with purpose, followed by Args, Returns, and Example sections. Each section earns its place, though the Returns section is slightly redundant with the output schema.

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 labeling tool with an output schema, the description covers purpose, all parameters, and return shape. It does not explicitly compare with sibling watermark tools or cover edge cases, but it provides sufficient context for an agent to decide when and how to call it.

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 schema has 0% description coverage, but the description fully compensates by explaining each parameter's meaning, including the exemption implications for is_artistic_work and is_satirical, valid language codes, and the default. The example further clarifies expected usage.

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 uses a specific verb ('Generate') and clearly identifies the resource ('deepfake label') and scope ('AI-generated or manipulated videos per Article 50(4)'). It distinguishes the tool from siblings like label_image_deepfake and label_audio_deepfake by explicitly targeting videos.

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 provides clear context for when to use the tool: for labeling AI-generated/manipulated videos under Article 50(4). It also mentions exemption criteria for artistic/satirical works. It does not explicitly name sibling alternatives, but the video-specific wording implies the boundary.

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