label_image_deepfake

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

With no annotations, the description carries the full burden. It discloses the output (label text, placement guidance, compliance info) and implies a non-destructive operation (generating guidance, not modifying the image). While it does not explicitly state side effects or limitations, the nature of the tool is clear and adequately transparent.

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 with a concise summary, a clear Args section, and an example. It is front-loaded with the main purpose and contains no unnecessary repetition or fluff. Every sentence adds value.

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 purpose, parameters, return values, and includes an example. It does not discuss error handling, edge cases, or interactions between the exemption flags, but for a labeling tool with a known output schema, this is reasonably complete.

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 has no descriptions (0% coverage), but the description's Args section thoroughly explains each parameter (image_description, is_artistic_work, is_satirical, language) and includes a usage example. This adds significant meaning beyond the schema and fully compensates 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 clearly states the tool's function: 'Generate deepfake label for AI-generated or manipulated images per Article 50(4).' This uses a specific verb (Generate) and resource (deepfake label for images), and distinguishes it from sibling tools like label_video_deepfake and label_audio_deepfake by explicitly focusing on images.

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 this tool: for labeling AI-generated or manipulated images. It does not explicitly exclude video/audio or mention alternatives, but the media-specific scope makes the intended usage obvious, especially when viewed next to sibling tool names.

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