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generate_scenario

Plan a multi-scene video from a brief. An LLM director returns per-scene English video prompts with durations and a soundtrack prompt.

Instructions

Plan a multi-scene video from a brief: an LLM director returns a structured scenario — per-scene English video prompts with durations, plus a soundtrack prompt. Feed each scene prompt to generate_video, then stitch the finished clips with compose_video. Charged in credits like chat (LLM usage).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoLLM model slug (omit to use the default).
styleNoOptional visual style, e.g. 'cinematic', 'documentary', 'product ad', 'anime'.
promptYesWhat the final video is about: product, story, mood, audience. Any language.
scene_countNoNumber of scenes. Omit to derive from duration (~5s per scene).
aspect_ratioNo9:16
duration_secondsNoTarget total length of the final video.
Behavior4/5

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

Annotations indicate non-read-only, non-idempotent, and openWorldHint=true. The description adds that it is 'Charged in credits like chat (LLM usage),' providing cost transparency. It does not contradict annotations. It could detail output structure more, but the behavioral traits are adequately disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very concise: two sentences, front-loaded with purpose and output, followed by workflow and cost. Every sentence is necessary and no extraneous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a planning tool with no output schema, the description effectively communicates the output (structured scenario) and the integration with sibling tools. It also covers cost. However, it could be more complete by specifying the exact output format or providing an example, but it is sufficient for understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 83% coverage with parameter descriptions. The description does not add significant new meaning beyond what the schema provides; it only implicitly references the 'prompt' parameter. With high schema coverage, baseline is 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Plan a multi-scene video from a brief.' It details the output (per-scene prompts, durations, soundtrack prompt) and distinguishes itself from sibling tools like generate_video and compose_video by specifying the workflow.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells when to use the tool (planning from a brief) and provides the subsequent steps: feed scenes to generate_video and stitch with compose_video. It also mentions cost context. However, it does not explicitly state when not to use or list alternatives, though sibling tools are available.

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

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