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Generate an image

generate_image

Generate a still image from a prompt with a hosted model and put it on the timeline as an image item. The still takes its place on the timeline at start straight away, and the finished image fills it when the run ends. Quality standard (default) or best (holds fine detail and spells lettering, signage and title text reliably; costs more). image lists project images (id or file name) to redraw or take from, such as a product shot to put in a new scene; standard takes up to 6, best up to 4, and an edit at best costs more than drawing from text. durationSeconds is how long the still sits on the timeline (default 5). Returns at once with the generation id; generation_status with wait: true waits on the run and fills the still when it ends. Costs credits; the error says what to fix.

Input Schema

NameRequiredDescriptionDefault
nameNoItem name; default: the prompt, shortened
waitNotrue waits for the result in this call (a long run hands back a job); default: return at once
imageNoImages to redraw or take from: media ids or names
modelNoExact image model id; overrides quality
startNoTimeline seconds; default: after the track’s last clip
trackNoTrack name or id; default: first video track or a new "Generated" one
aspectNoDefault: the project’s, or the closest the model renders
promptYesWhat the image should show; quote any words it has to spell
projectYesProject id or exact name
qualityNo
durationSecondsNoHow long the still sits on the timeline

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already mark this as a non-read-only, non-destructive write; the description adds real context beyond them: the placeholder appears on the timeline immediately and is filled when the run ends, credits are consumed, capacity differs by quality (6 vs 4 inputs), and best-quality edits cost more. It stops short of describing failure handling or what remains if a run fails.

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?

Front-loaded with the core action and side effect, then cost/quality guidance, then parameter notes. The sentences are long and carry several clauses each, but every clause adds information rather than padding.

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?

For an 11-parameter mutation with no output schema, the description covers the essential unknowns: what gets created on the timeline, what is returned immediately (generation id), and how to block for the result. Failure semantics and credit quantity are left vague, but enough is present to call the tool correctly.

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 91%, so the baseline is 3, but the description still adds meaning: `quality` tradeoffs (lettering/signage reliability), per-quality limits on `image`, and the default of 5 for `durationSeconds`. It leaves `model`, `aspect`, and `track` to the schema, which already documents them.

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 first sentence gives a specific verb and resource ('Generate a still image from a prompt') plus the concrete side effect ('put it on the timeline as an image item'). This clearly separates it from siblings like generate_video and apply_timeline.

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?

It states when to pick `standard` vs `best`, when to pass `image` (redraw or source from an existing shot), and how to get a synchronous result via generation_status with `wait: true`. It never says when not to use it, e.g. explicitly deferring video needs to generate_video, but the routing context is otherwise clear.

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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