Video-01 — generate short cinematic videos from text prompts
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Text prompt for generation | |
| prompt_optimizer | No | Use prompt optimizer | |
| first_frame_image | No | First frame image for video generation. The output video will have the same aspect ratio as this image. | |
| subject_reference | No | An optional character reference image to use as the subject in the generated video (this will use the S2V-01 model) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It accurately states the core behavior but omits significant details such as model differences (S2V-01), aspect ratio handling, or limitations like 'short' video length.
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 a single, front-loaded sentence that is efficient and easy to parse. It could provide more context, but it is not overly 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?
With 4 parameters, no output schema, and no annotations, the description is too sparse. It fails to explain the different video generation modes (e.g., first frame, subject reference) or what the user should expect from the output, making it inadequate for full understanding.
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 coverage is 100% with all parameters described in the input schema. The description adds no extra meaning beyond what the schema already provides, meeting the baseline.
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?
Description clearly states the tool generates short cinematic videos from text prompts, using a specific verb and resource. This distinguishes it from sibling tools which are image generators or TTS.
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?
Usage is implied by being the only video generation tool, but no explicit guidance on when to use it versus alternatives or how to choose between first_frame_image and subject_reference is provided.
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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Six of eight tools are for image generation, with overlapping capabilities and only subtle differentiators (speed, quality, text rendering). An agent cannot easily determine which model to use for a generic image generation request, leading to high ambiguity.
All tool names follow the same snake_case pattern using provider_model format (e.g., black_forest_labs_flux_pro, stability_ai_sdxl). The convention is uniform across the entire set, making it predictable and easy to parse.
The count of 8 tools is within the ideal range for a media studio, but the heavy redundancy of image generation models makes the set slightly bloated. Still, the overall scope is clear and manageable.
The tool surface covers the primary media generation modalities—image, video, and text-to-speech—which aligns with the 'Media Studio' purpose. Minor gaps exist (e.g., no image editing or audio-to-text), but core generation workflows are well covered.