watermark_text

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

With no annotations provided, the description carries the full burden. It discloses that the tool 'adds machine-readable metadata' and returns 'a dictionary containing the watermarked text with embedded metadata.' This gives a clear behavioral model, though it doesn't mention side effects like possible text modifications or reversibility.

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 a clear breakdown of arguments, return value, and an example. Every sentence provides value without fluff, and the structure is 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?

The description covers the legal/regulatory context (EU AI Act), parameter semantics, return type, and gives a concrete example. Given the tool's simplicity (3 parameters, flat structure) and the presence of an output schema, this is fully sufficient for an agent to select and invoke it 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?

Schema description coverage is 0%, so the description must compensate. It explains every parameter (text_content, generator, format_type) with explicit meanings, defaults, examples (e.g., 'GPT-4' for generator, 'plain, markdown, html' for format_type), and even includes a usage example.

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 action ('Add metadata watermark'), the target resource ('AI-generated text'), and the specific compliance purpose ('EU AI Act Article 50(2)'). This distinguishes it from sibling tools like watermark_image, watermark_video, and watermark_audio, which handle other modalities.

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 strong context by stating this is 'required for provider compliance with Article 50(2) for text content generation systems.' It implies usage for text content but does not explicitly name alternatives or exclusion criteria, so it falls short of a 5.

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