get_emotion_recognition_disclosure

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

With no annotations provided, the description carries the burden. It discloses that the text MUST be shown to users (compliance requirement) and that it returns a dictionary with disclosure text and metadata. This conveys the read-only, non-destructive nature and the expected output shape, though it doesn't mention edge cases or error behavior.

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 Args, Returns, and an Example section. Every sentence adds value, and the format is easily parseable for an AI agent. No fluff.

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?

This is a simple retrieval tool with two optional parameters, and the description provides complete context: purpose, parameter options, defaults, return type, and an example. No additional metadata is needed for correct invocation.

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 coverage is 0%, but the description fully compensates by documenting each parameter: language accepts en, es, fr, de, it; style accepts simple, detailed, privacy_notice. It also provides defaults and an example call, making the parameter semantics exceptionally clear beyond the bare schema.

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 uses a specific verb+resource combination ('Get emotion recognition disclosure text') and clearly scopes it to EU AI Act Article 50(3) compliance. It is distinct from sibling tools like get_ai_interaction_disclosure or get_deepfake_label_templates, which cover different disclosure types.

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 states when to use it (when an AI system uses emotion recognition technology and must show disclosure text). It does not explicitly name alternatives or exclusion criteria, but the purpose is clear enough that a link to siblings is not necessary.

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