acttrace_generate_transparency_notice

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

With no annotations provided, the description carries full burden for behavioral disclosure. It explains the output (user-facing copy, placement, caveats, human-review recommendation) and notes the notice is a draft, not legal advice. However, it does not explicitly state that the tool is read-only or describe any side effects, permissions needed, or rate limits, leaving some transparency gaps.

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 concise and well-structured: it starts with the main purpose, then outlines output details, usage guidance, and parameter descriptions in a logical order. Every sentence adds value without redundancy, making it easy to scan for key information.

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?

Given 7 parameters, 2 required, and an output schema, the description covers all aspects: input details, output content, usage context (domain, non-legal status), and constraints (language limitation for MVP). It provides enough information for an agent to invoke the tool correctly without relying on external documentation.

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 fully describes each parameter, including allowed values (e.g., notice_type options), defaults (tone, language), and optionality (feature_name, human_review_level, risk_category). This adds significant meaning beyond the raw schema, enabling correct parameter selection.

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 purpose: generating an EU AI Act Article 50 transparency notice. It specifies the verb 'Generate' and the resource 'transparency notice', and distinguishes from the sibling tool 'acttrace_classify' by detailing the output content such as user-facing copy and placement.

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 explicit usage examples ('AI transparency notice', 'Article 50 notice') and clarifies the tool's domain (non-financial SaaS/technology products) and limitations (not legal advice). It lacks explicit exclusions or alternative tool recommendations, but the context is clear enough for appropriate selection.

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