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hermoso-ai

Hermoso

Official

Generate video

generate_video

Render a RAW video clip from a text prompt and return its served mp4 URL. Edit an existing video by providing a transformation instruction and the source clip URL.

Instructions

Render a RAW video clip from your own prompt and return its served mp4 URL. For finished brand ADS prefer render_ad (it runs the Studio quality pipeline — composited text, clean speech, end card, music); use this for raw/experimental clips or precise manual control. ONE generation = one continuous clip up to the model’s longest listed duration (seedance-2 goes to 15s single-pass with a full multi-beat arc — never assume a generic 8–10s cap); durationSeconds must be one of the model’s durations from hermoso_capabilities. Renders take 1–3 min. refImage anchors the opening frame; ttsScript adds a voiceover. Pass refVideo (a clip URL) to EDIT an existing video instead of generating from scratch — the omni engine transforms that clip per your prompt, inheriting the source clip’s canvas + length (aspectRatio/durationSeconds are ignored for an edit). Spends credits (Starter plan is video-blocked server-side).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNovideo model id from hermoso_capabilities. Naming one is a DELIBERATE pick — the server asks before ever swapping it (no silent fallback); omit it to let the router pick
promptYesthe video prompt / shot description (for a refVideo edit, this is the transformation instruction)
refImageNolocal path or URL to anchor the first frame
refVideoNoURL of an existing video to EDIT rather than generate from scratch — the omni engine accepts a raw clip and transforms it per your prompt, inheriting the SOURCE clip’s canvas (aspect ratio) and length (aspectRatio/durationSeconds are ignored for an edit). Omit to generate a fresh clip.
ttsVoiceNovoice name, e.g. Rachel / George
musicMoodNolicensed music-bed mood (e.g. upbeat / cinematic) — omit for no music bed
ttsScriptNovoiceover script to speak
resolutionNo'720p' default; '480p' = cheap fast draft pass, '1080p'/'4k' = premium final delivery (more credits)
aspectRatioNodefault '9:16'
durationSecondsNoclip length in seconds

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rawNothe raw job result payload (e.g. images[] for carousel template ads)
urlNothe served URL of the finished media (absent/null while still rendering)
jobIdNothe render job id — poll get_job with this id to resume or inspect
modelNothe product-facing label of the model that rendered it
stillRenderingNotrue when the render is still in progress — keep polling get_job with jobId
Behavior5/5

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

Discloses that renders take 1–3 min, spends credits, and that the Starter plan is video-blocked. For editing via refVideo, it notes that aspectRatio/durationSeconds are ignored and the source clip's canvas and length are inherited. This adds value beyond the annotations.

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

Conciseness4/5

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

The description is somewhat lengthy but well front-loaded with the main purpose. Every sentence provides unique value, though minor restructuring into paragraphs could improve readability. Still, it is efficient and informative.

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

Completeness5/5

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

Given the tool's complexity (10 parameters, 1 required, output schema exists), the description covers generation vs. edit, timing, credit implications, and references external capabilities. It is comprehensive and leaves no major gaps.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds critical context: durationSeconds must match model durations from hermoso_capabilities, refVideo editing ignores certain parameters, and the behavior of model selection. It clarifies the purpose of each parameter beyond the schema.

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 renders a RAW video clip from a prompt and returns an mp4 URL. It distinguishes itself from the sibling 'render_ad' which is for finished brand ads, and mentions editing via refVideo.

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

Usage Guidelines5/5

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

Explicitly states when to use this tool (raw/experimental clips, precise manual control) versus 'render_ad' (finished brand ads). Also provides guidance on model duration constraints, credit spending, and editing behavior.

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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