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 discloses polling behavior (async), return values (verdict, confidence, full result), and optional features (e.g., zero-retention mode auto-deletes media). However, it does not discuss error handling, rate limits, or whether the operation is read-only. This is moderate transparency, neither excellent nor poor.

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 two sentences long, front-loading the core purpose and then detailing the polling behavior and optional flags. It is concise with no redundant information, though the parameter listing could be more structured. It earns its place without being verbose.

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 9 parameters and no output schema, the description explains the return values (verdict, confidence, full result) and covers key behaviors (polling, optional features). However, it omits error scenarios, the structure of 'full result', and comparative guidance against siblings. It is adequate but not fully comprehensive for the tool's complexity.

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?

With 0% schema description coverage, the description must compensate. It adds meaning for most parameters: explains flags like run_intelligence ('adds media intelligence'), zero_retention_mode ('auto-delete media'), and lists model_type options. However, it does not mention max_wait_seconds, leaving a gap. Overall, it adds significant value 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 clearly states 'Detect whether media (audio, image, or video) at a public HTTPS URL is a deepfake / AI-generated', providing a specific verb (detect) and resource (media) with clear scope (deepfake/AI-generated). It distinguishes itself from sibling tools like detect_watermark or trace_audio_source by focusing on deepfake detection.

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 implies usage by stating it polls to completion and lists optional features, but it does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or prerequisites beyond a public URL. The model_type parameter is mentioned but not explained in terms of when to choose each option.

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