get_deepfake_label_templates

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

No annotations are provided, so the description carries the full burden. It clearly implies a read-only operation through 'Get' and 'returns', and discloses the return structure ('Dictionary containing all available labels organized by content type') and the default language behavior. It does not mention side effects, but for a simple retrieval tool the non-mutating nature is evident.

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 one-sentence summary, a usage note, Args, Returns, and an Example. Every sentence serves a purpose, and the example clarifies invocation. No redundant or vague phrasing.

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 the tool's simplicity (one optional parameter, no required inputs) and the presence of an output schema, the description covers all necessary aspects: what it does, when to use it, the parameter format, and return shape. The example anchors the usage. No significant gaps remain.

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 schema only defines 'language' with a default and no description, so schema coverage is 0%. The description compensates by listing the allowed language codes ('en, es, fr, de') and the default, which is the full parameter semantics needed. It could add what language affects (e.g., localized label text), but the provided info is sufficient.

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 the specific verb 'Get' with a clear resource: 'all available deepfake and AI-generated content labels.' It also clarifies scope by naming content types (images, videos, audio, text), which distinguishes it from sibling labeling/watermarking tools that apply labels rather than retrieve template options.

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 explicitly states 'Use this to see what labels are available for images, videos, audio, and text,' giving clear context for when to call this tool. It does not name alternative tools, but the purpose is distinct enough from the sibling labeling tools that no explicit exclusion is necessary.

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