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

Annotations only mark this as a non-read-only, non-destructive mutation. The description adds substantial behavioral context: it is long-running (several minutes), returns job_guids immediately, and yields per-language audio_url and final_video_url outputs — critically the async polling requirement, which nothing structured conveys.

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?

Three front-loaded sentences: purpose first, then per-language return values, then timing and follow-up steps. No filler; every clause (YouTube multi-language track, alignment, polling) carries operational value.

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?

With no output schema, the description carries the return-shape burden and does so (audio_url, final_video_url, timeline alignment). It also covers async timing and the required polling tool, leaving nothing an agent needs to invoke and follow up 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 100%, and each parameter already carries a description (voice tokens, language codes, video_url source, volume semantics). The description adds no parameter-level detail beyond the schema, so the baseline 3 applies.

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?

States a precise verb and resource ('Full automatic dubbing of an uploaded video') and enumerates the pipeline (transcribe, translate, voice, render per language). This clearly separates it from text_to_speech, multi_voice_to_speech, and transcribe_media siblings.

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?

Explicitly routes the agent to get_localization_status for polling and estimate_video_localization for cost on long videos. It gives clear workflow context but does not state when NOT to use it or contrast directly against a close alternative.

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