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generate_video

Generate a short video from a text prompt and save it to disk.

Instructions

Generate a short video from a text prompt and save it to disk. Asynchronous: the provider renders in the background and this call waits, typically one to three minutes. Call it only when the user explicitly asks for a video — reference images and videos are not supported. Returns the absolute path of the saved file, the task id and how long it waited.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoVideo model id for this call only. Omit to use the configured default. Call get_config for the available ids and their notes.
ratioNoAspect ratio as W:H, for example 16:9 or 9:16. Default 16:9.
promptYesWhat to film, in natural language. Describe the subject, the action and the camera movement: a shot the model can stage beats a list of keywords.
durationNoClip length in seconds. Default 5. Longer clips take longer and cost more; the provider decides which lengths it accepts.
outputDirNoDirectory for this call only. Omit to use the configured default, which is <workspace>/video_output.
timeoutMsNoHow long the whole call may wait — create, poll, download — in milliseconds. Omit to use the configured default. A five second clip measured about four minutes, so keep this generous; the ceiling is 3600000 (one hour).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the full behavioral burden. It discloses that the call is asynchronous and waits one to three minutes, that it saves to disk, and that it returns the absolute file path, task id, and wait duration. This is strong transparency, though it could also mention details like overwrite behavior or failure conditions.

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?

Three sentences with no fluff: purpose, async behavior, invocation condition, and return values are each covered. The most decision-relevant information is front-loaded, and every sentence earns its place.

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 with six parameters, no annotations, and no output schema, this description is complete enough for an agent to select and call it correctly. It explains the operation, the asynchronous waiting behavior, the supported input scope, and the return values, while the schema fully documents each parameter.

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?

Input schema coverage is 100%, so the baseline is 3. The description adds little parameter-specific meaning beyond the schema; it only reinforces that prompts are text-only via the unsupported-reference note. The schema descriptions themselves are already detailed, so no compensation is needed.

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 uses a specific verb and resource: 'Generate a short video from a text prompt and save it to disk.' It also clearly distinguishes itself from sibling tools by stating that reference images and videos are not supported, which separates it from generate_image and any editing/referencing workflows.

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?

The description gives explicit when-to-use guidance: 'Call it only when the user explicitly asks for a video.' It also specifies exclusions ('reference images and videos are not supported'), but it does not explicitly name alternative tools such as generate_image for image-generation cases, so it falls just short of full routing guidance.

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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