label_news_text

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 explains the tool's purpose and returns a dictionary with labeled text and compliance info, but it does not disclose potential side effects, permission requirements, or whether input text is modified in place. This is adequate but not rich with additional behavioral context.

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 front-loaded with a one-sentence summary followed by an optional compliance context, an Args block, Returns, and Example. The structure is clear and each section adds value, though the compliance sentence slightly overlaps with the opening sentence. Overall, it is concise and well-organized.

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 text labeling tool with a relatively simple operation, the description covers purpose, inputs, output, and provides an example. The output schema exists (as noted), so the Returns section is an extra benefit. It lacks details on edge cases or the exact disclosure format, but it is sufficiently complete for most agentic use cases.

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 description provides meaningful explanations for all four parameters, compensating for the 0% schema description coverage. It clarifies that text_content is the AI-generated text, has_human_editor is an exemption qualifier, editor_name is applicable only if an editor exists, and language includes a default and allowed values (en, es, fr, de). This exceeds the bare schema titles and types.

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: adding AI-generated content disclosure to news articles and public interest text. It references EU AI Act Article 50(4) compliance, which is a specific and distinct purpose compared to sibling tools like watermark_text or label_image_deepfake, making it easy to distinguish.

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 specifies the target content ('news, journalism, or public interest content'), providing clear contextual guidance for when to use this tool. However, it does not explicitly mention when not to use it or compare it to alternative siblings like watermark_text, so it stops short of full exclusions and alternative naming.

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