label_audio_deepfake

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

Given the lack of annotations, the description carries the full burden. It discloses what the tool returns (label text, placement guidance, compliance info), mentions formats (spoken/written), and notes the artistic work exemption. It does not describe side effects or limitations, but for a read-only label generation tool, this is adequate.

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 well-structured and efficient. It fronts the core purpose, then details args, returns, and an example. Every sentence contributes value without redundancy, and the length is appropriate for the tool's complexity.

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 covers all essential aspects: purpose, parameters, return format, placement guidance, and a concrete example. With an output schema present and no nested objects, this level of detail is complete. The mention of Article 50(4) and exemption adds regulatory context that a user needs.

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?

Despite 0% schema description coverage, the description fully explains each parameter in the Args section, including the language codes, the purpose of audio_description, and the exemption implication of is_artistic_work. The example further illustrates usage, completely compensating for the schema's lack of descriptions.

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 opens with a specific verb and resource: 'Generate deepfake label for AI-generated or manipulated audio per Article 50(4).' This clearly states the tool's function and scope, distinguishing it from sibling tools like label_image_deepfake and label_video_deepfake.

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 context is explicitly tied to audio content and Article 50(4), making the intended use clear. However, it does not explicitly state when not to use it or name alternatives, though the focus on audio inherently differentiates it from the visual labeling siblings.

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