Skip to main content
Glama

Turn an image into a video

animate_image

Convert a finished Midjourney grid image into a short video clip. Add motion prompts to direct camera moves; choose resolution and speed to control GPU cost.

Instructions

Animate one image from a finished grid into a short video.

Leave the prompt out and Midjourney decides the motion. Give it one and it follows that instead, which is how you get a specific camera move rather than whatever the model felt like.

Video takes longer than a still, so expect to wait. 480p is quicker and cheaper than 720p. Costs GPU time.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
waitNoBlock until it finishes. Defaults to true.
indexYesWhich image, zero-based.
speedNoGeneration speed. 'fast' burns fast-hours and takes under a minute. 'relax' is unlimited on Standard and above but queues, often for several minutes. 'turbo' is quickest and costs double. Defaults to MIDJOURNEY_DEFAULT_SPEED, itself 'fast'.
job_idYesThe finished job holding the image.
motionNoHow it should move, for example 'slow push in, hair drifting'. This is appended to the image's own prompt, because Midjourney reads that field as the scene rather than the movement. Omit to let it choose.
confirmNoMust be true for this to run. This call spends GPU time from the Midjourney plan, or cannot be undone, so it is refused without an explicit confirmation.
resolutionNoDefaults to 480.
timeout_msNoHow long to wait. Video is slower than a still.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.3.0

TDQS

A4/5.0
Behavior4/5

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

Annotations declare non-readonly, non-idempotent, open-world, non-destructive, so the mutation/repeat behavior is already encoded. The description adds genuinely useful context beyond that: GPU-time cost, longer wait than a still, resolution-driven speed/cost tradeoff, and how the motion field is merged into the image's own prompt.

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?

Three short paragraphs, purpose front-loaded, no wasted preamble. Casual phrasing like 'whatever the model felt like' is slightly chatty but still informative rather than filler.

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

Completeness3/5

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

For an 8-parameter job-creating tool with no output schema, the description covers cost, timing and motion semantics but says nothing about what the call returns (job id, status) or how to retrieve the finished video via siblings like get_job/wait_for_job. That is a real gap, though less severe since the schema and annotations carry the safety profile.

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

Parameters3/5

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

Schema description coverage is 100%, so baseline is 3. The description adds a little meaning for the motion/prompt distinction, but it confusingly calls the motion input 'the prompt', which does not match the parameter name and risks misleading the agent about which field to populate.

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+scope: 'Animate one image from a finished grid into a short video.' The mention of 'a finished grid' and 'one image' clearly separates it from siblings like vary_image, upscale_image, pan_image and remix_image, which operate on images differently.

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?

Explains when to supply motion versus omit it, and the 480p vs 720p speed/cost tradeoff, and notes that it operates on a 'finished' job. It does not, however, name an alternative tool or state when a user should pick upscale/vary/rerun instead, so routing is left implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.