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Generate video

generate_video

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 — the longest-clip model in the catalog today renders a full multi-beat spot of up to 30 SECONDS in ONE unbroken take with native synchronized audio, so never assume a generic 8–10s cap and never stitch something that fits one clip; durationSeconds must be one of the model’s durations from hermoso_capabilities, which is the live list. TO GET A SPECIFIC MODEL, NAME IT in model: an unnamed render is routed by the server’s own auto-pool, which is narrower than the catalog, so the longest-clip and highest-resolution models are reached by naming them and not by omitting the field. Renders take 1–3 min. refImage anchors the opening frame; ttsScript adds a voiceover. AUDIO IS NOT FREE AND NOT OPTIONAL BY DEFAULT: a clip delivered with no audio of its own gets a music bed composed and CHARGED on top of the render (see musicMood and audio) — on a cheap short draft the bed can cost as much as the clip. 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). RAW MODEL ACCESS: your prompt is NOT dispatched verbatim by default — a few small guards are appended (packaging/label safety when no reference image rides, a negative prompt on the models that take one, reference-binding lines when references ride) and hex colour codes are rewritten to colour names. Pass raw:true for none of that. raw:true dispatches your prompt to the model BYTE-IDENTICAL — no rewriting, no appended guidance, no negative prompt, no brand references attached on your behalf. Credits, the durable delivery of the finished asset and the per-model validation are unchanged. Spends credits (Starter plan is video-blocked server-side).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rawNoRAW MODEL ACCESS: dispatch this prompt to the model BYTE-IDENTICAL — no appended packaging/label guidance, no negative prompt, no reference-binding lines, no hex-to-colour-name rewrite. Use it when you want the model itself rather than Hermoso's render craft. Two provider-mandated corrections still apply, because the vendor hard-fails without them: an @ImageN token that outnumbers the references actually shipped is dropped, and a prompt past the endpoint's published character cap is trimmed at a sentence boundary. Billing, durable delivery and per-model validation are unchanged.
audioNodefault true. false = render SILENT: no native model audio, no music bed, and no bed charge held or billed. This is the ONLY way to decline the automatic bed (see musicMood) — leave it alone for anything that should have sound, and do not combine it with ttsScript.
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
musicMoodNoWHICH mood the music bed is composed in (upbeat / calm / warm / epic / tense / playful / elegant / hype / chill / dramatic). It does NOT decide WHETHER there is one: a clip that comes back with no audio track — every model hermoso_capabilities lists as "silent", plus any audio model that returned mute — gets a bed composed and CHARGED automatically, at the flat per-track fee hermoso_capabilities reports as explainerMusicCredits, and omitting this field only means the mood defaults to "warm". Pass audio:false for a genuinely silent clip with no bed and no bed charge.
ttsScriptNovoiceover script to speak
resolutionNo'1080p' default (what we ship and bill for); '480p'/'720p' = cheaper draft passes, '4k' = premium final delivery (more credits). NOT EVERY MODEL OFFERS EVERY TIER — this enum is what the tool accepts, and each model's OWN `resolutions` list in hermoso_capabilities is what it can actually render (the longest-clip 30s model, for one, tops out at 720p). Ask for a tier the chosen model does not list and it is rendered at that model's best available tier instead, with nothing in the reply saying so — so check `resolutions` before promising anyone 1080p or 4k.
aspectRatioNodefault '9:16'
durationSecondsNolength of THIS ONE clip in seconds — pick one of the CHOSEN model’s listed durations from hermoso_capabilities (never a generic guess: the lists differ per model, from 4–8s on the short models up to 30s on the longest-clip one). This is a single continuous generation, so it CANNOT exceed that model’s longest clip: a longer ask is REFUSED with nothing rendered and nothing charged (it is never quietly truncated). Past ~15s only the long-clip models qualify, and an unnamed render is routed by the narrower auto-pool — so NAME the model in `model` when you are asking for a long single take. For a spot longer than any one clip, use plan_ad with durationSeconds then render_ad, which stitches acts of at most one model clip each (on a 15s-clip model, 40s = 15+15+10).

TDQS

A4.8/5.0
Behavior5/5

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

With all annotations false, the description carries the full burden, and it does so thoroughly. It discloses non-obvious behaviors: audio is not free and gets a music bed charged unless audio:false, raw:true changes prompt handling, duration limits cause refusal rather than truncation, and editing ignores certain parameters. These are exactly the behavioral traits an agent needs to know.

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

Conciseness3/5

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

The description is extremely long and dense, forming a single paragraph with many intertwined points. While each sentence adds value, the lack of structure and the sheer volume make it hard to parse. It is not concise; it would benefit from bullet points or section breaks. However, it is front-loaded with the core purpose, so it earns a mid-range score.

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?

For a tool of this complexity (12 parameters, multiple modes, billing implications), the description is exceptionally complete. It covers return format, per-model capabilities, edit behavior, audio billing, and raw access nuances. No critical information appears missing; an agent can call it correctly without external lookup.

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?

Despite 100% schema coverage, the description adds significant meaning beyond the schema. For durationSeconds, it explains per-model duration lists and refusal behavior; for resolution, it clarifies per-model tier limitations and silent fallback; for audio, it details default music bed charging. This context is essential for correct invocation and goes well beyond the raw parameter descriptions.

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 states a specific verb and resource: 'Render a RAW video clip from your own prompt and return its served mp4 URL.' It also distinguishes itself from render_ad by naming the alternative and its use case, so an agent can immediately tell when this tool is appropriate. The purpose is unambiguous and clearly differentiated.

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 vs. alternatives: 'For finished brand ADS prefer render_ad... use this for raw/experimental clips or precise manual control.' It also covers editing (refVideo) and the need to name a model for long clips, providing clear context for the tool's place among siblings.

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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TDQS

A3.7/5.0
Disambiguation2/5

With 293 tools, the surface is enormous and many tools have overlapping purposes—multiple posting tools (post_to_meta, post_to_linkedin, schedule_post, etc.), multiple analytics tools per channel, and several search tools (search_meta_ads, search_instagram, search_reddit...). While each description is detailed, the volume makes it difficult for an agent to reliably distinguish between similar tools without careful reading, leading to frequent misselection.

Naming Consistency4/5

The naming is largely consistent with a verb_noun pattern (post_to_*, list_*, create_*, delete_*, update_*, manage_*). There are clear families for major operations. A few outliers like 'google_business_account', 'hermoso_capabilities', and 'store_get' break the pattern, but the overwhelming majority follow a predictable structure, making navigation somewhat easier.

Tool Count1/5

293 tools is far beyond any reasonable scope for a single MCP server, even for a comprehensive marketing platform. The calibration guide flags 50+ as an extreme mismatch, and this is nearly six times that threshold. Such a large surface overwhelms context windows, increases the probability of misselection, and makes it impractical for agents to learn or use effectively.

Completeness4/5

The tool set covers a vast domain: ad creation and rendering, posting across nine+ social channels, analytics and reporting, file management (Drive/OneDrive), competitor research, brand management, and more. It appears to provide CRUD and lifecycle coverage for most resources. While there may be minor gaps given the immense scope, the overall coverage is impressively comprehensive.