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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It adds important context by stating that batch size defaults to configuration and is capped at 4, and that video jobs are long. It also mentions the JSON response includes per-prompt outputs and errors. However, it omits operational details such as runtime expectations, failure handling beyond 'errors', or the impact of the batch cap. This is useful but incomplete for a tool with zero annotation coverage.

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 three sentences, front-loaded with the core action, followed by use cases, and ends with a brief note on defaults and return format. Every sentence earns its place with no redundancy.

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?

The output schema exists, so the description need not detail return values, though it briefly mentions the JSON response structure. However, with eight parameters and zero schema descriptions, the description leaves significant gaps in what each parameter means and how to use them. For a batch generation tool with no annotations, more context on parameter usage and failure modes would be expected.

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 explains batch_size defaults and capping, which adds meaning beyond the schema for that parameter. It also references 'reference-guided variations' and mentions 'aspect-ratio or resolution comparisons' hinting at aspect_ratio and resolution parameters, but it does not explain the other parameters (task, prompts format, enhance_prompt, reference_image_paths, delivery). Baseline for low coverage would require full compensation; the description partially compensates but leaves most parameters undocumented.

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 action (generate multiple videos in parallel batches) and the resource (Gemini Omni Flash videos). It names the sibling tool only implicitly by contrasting 'multiple' with the single-video context of generate_video. The purpose is specific enough that an agent can distinguish this as a batch version of video generation, though it doesn't explicitly say to use the sibling for single prompts.

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 gives concrete use cases (storyboards, aspect-ratio or resolution comparisons, reference-guided variations), which clearly indicate when this tool is appropriate. It does not explicitly say when not to use it or name generate_video as the alternative for single videos, so it falls short of a 5.

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

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

With no annotations, the description carries the full behavioral burden and does so: it discloses that outputs are MP4 3-10s at 24fps, SynthID-watermarked, that resolutions above 720p are upscaled, that unsupported parameters (temperature, top_p, negative_prompt, voice edits, YouTube sources) will not work, and that uploaded-video editing is region-restricted. It also explains the task-omission interaction with previous_interaction_id, which is a non-obvious API constraint an agent would otherwise hit as an error.

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?

Well organized under Capabilities, Parameters, Prompt tips, Limitations, and Returns headings, with the purpose front-loaded in sentence one. It is long, and the Prompt tips section is arguably beyond the minimum needed to invoke the tool, but for a generative tool with many modes that guidance is largely load-bearing rather than filler.

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, zero schema descriptions, no annotations, and a rich output schema, the description is complete: it covers all inputs, the behavioral constraints, and the failure modes, and it defers return-value detail to the output schema while still noting the key fields (video.path, interaction_id). Nothing an agent needs to call this correctly is missing.

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% across 9 parameters, so the description must compensate entirely, and it does: every parameter is explained with meaning, defaults, and accepted values (task modes, 16:9/9:16, 360p/720p/1080p/4k, up to 6 reference images, delivery uri vs inline, enhance_prompt default false). It even supplies prompt-syntax guidance (<FIRST_FRAME>, <LAST_FRAME>, <IMAGE_REF_N> tags) that the bare string-typed schema cannot express.

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 opening sentence names a specific verb pair (generate or edit), the resource (MP4 videos), and the exact backing model (gemini-omni-1.1-flash), then enumerates the five distinct modes. This is unambiguous about what the tool does. It loses the top mark only because it never differentiates itself from the sibling tool batch_generate.

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?

It gives clear per-mode selection guidance (text_to_video vs image_to_video vs reference_to_video vs edit vs extend) and an explicit when-not rule: omit task for edit/extend because the API rejects an explicit task alongside previous_interaction_id. That is strong intra-tool routing, but no alternative tool is ever named, so the cross-tool decision (this vs batch_generate) is left to inference.

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