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render_video

Render a video from a raw prompt with any model — the Creative Studio composer as a tool. SPENDS CREDITS. The prompt is sent to the provider VERBATIM, so this is the tool to use when you want exact control rather than a template clone. Returns a job id immediately; call get_generation to follow it. Some models TRANSFORM media instead of generating (upscale, remove-bg, face-swap): they take NO prompt at all. Call list_models first and read prompt ('required'|'optional'|'none') and requires — for prompt:'none' models omit prompt and pass the source media instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
audioNoNative audio. Omit to use the model default (usually on).
modelYesModel id from list_models, e.g. dreamina-seedance-2-ref. Required — there is no default.
aspectNoOnly values list_models reports for this model.
audiosNoReference media. Each entry is either a plain URL, or {name, url} so the prompt can address it as @name — the server renumbers those to the @Image1/@Video1 form the provider reads, in the order given, so you never count positions yourself.
imagesNoReference media. Each entry is either a plain URL, or {name, url} so the prompt can address it as @name — the server renumbers those to the @Image1/@Video1 form the provider reads, in the order given, so you never count positions yourself.
promptNoSent to the provider VERBATIM — nothing rewrites it. Provider flags parsed out of prompt text (--seed, --cf, --wm) survive, so they work here. Omit entirely for models whose list_models.prompt is 'none' (upscale, remove-bg) — they take no text.
run_idNoOptional. Your id for this agent run (1–64 of A-Z a-z 0-9 . _ : -). Stored on every job of the run so they can be found and filed together later. Use the same value for every call of one run.
videosNoReference media. Each entry is either a plain URL, or {name, url} so the prompt can address it as @name — the server renumbers those to the @Image1/@Video1 form the provider reads, in the order given, so you never count positions yourself.
projectNoOptional. A Drama Studio film id in the workspace being charged: the finished generation is added to that film's media pool (videos on its timeline, images/audio as references). Workspace generations only; checked before any credit is spent.
durationNoSeconds. Only values list_models reports for this model.
workspaceNoOptional. The workspace to generate in. Only a workspace API key (Settings → Developer) can spend a workspace — this may only repeat that key's workspace id; omit it and the key's own scope is used. A personal key or a sign-in token stays personal (passing a workspace is refused).
resolutionNoOnly values list_models reports for this model.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations only cover safety flags (readOnlyHint=false, destructiveHint=false, openWorldHint=true, idempotentHint=false); the description adds the materially useful traits: it SPENDS CREDITS, the prompt is sent verbatim with provider flags surviving, it returns a job id immediately (async, non-idempotent by design), and some models transform rather than generate. This goes well 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.

Conciseness5/5

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

Front-loaded with purpose, then cost warning, then routing rule, then workflow. Every sentence carries a distinct decision-relevant fact; nothing is padding despite the density.

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?

With 12 parameters, no output schema, and one required param, the description closes the gaps an agent would otherwise hit: cost, async job-id return with get_generation follow-up, prerequisite list_models lookup, and the prompt-less transform-model case. Nothing needed to call it correctly is missing.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description still adds actionable meaning: the verbatim-prompt contract, the need to derive aspect/duration/resolution from list_models, and the rule to omit prompt for prompt:'none' models while passing source media. This complements rather than merely repeats 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?

States a specific verb+resource (render a video) and immediately scopes it against siblings: 'exact control rather than a template clone' distinguishes it from clone_template, and the render_image/render_audio/render_effect family is implied by 'video'. An agent can select this without opening another schema.

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?

Explicit when-to-use ('raw prompt with any model... exact control'), a prerequisite workflow ('Call list_models first and read prompt and requires'), the follow-up tool ('call get_generation to follow it'), and a when-not case (prompt:'none' models omit prompt and take source media). Alternatives are named concretely.

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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