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modify_video

Modify a video by submitting a prompt and source video URL to RunAPI's Luma endpoint, then retrieve task status and output URLs.

Instructions

Create a Luma task on RunAPI (modify video). Returns a task id, status, and output URLs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
waitNoPoll until the task reaches a terminal status.
modelNoRunAPI model slug for this model line.
promptYes
watermarkNo
timeout_msNo
callback_urlNo
poll_interval_msNo
source_video_urlYes
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It only states that a task is created and returns an ID/status/output URLs, but fails to mention critical behavior such as asynchronous nature, the optional 'wait' parameter, polling, or potential side effects. This is insufficient for a task-creation tool.

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?

The description is a single sentence that front-loads the primary purpose and includes the return value. There is no fluff or redundant wording; every word earns its place.

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

Completeness2/5

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

The tool has 8 parameters and no output schema or annotations, yet the description provides only a minimal overview. It omits crucial contextual details such as how the task is executed, the meaning of key parameters, and the workflow implications (e.g., using get_task to poll). The description is too sparse for the tool's complexity.

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

Parameters1/5

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

Schema description coverage is only 25% (wait and model have descriptions), and the tool description adds no parameter meaning. It does not explain what 'prompt' or 'source_video_url' are for, nor mention parameters like 'timeout_ms' or 'callback_url'. The description fails to compensate for the sparse 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?

The description clearly states the action ('Create a Luma task') and the resource ('modify video'), and clarifies the platform ('on RunAPI'). It also mentions the return value, making it distinct from sibling tools like get_task (which retrieves) and check_pricing (which checks costs).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for modifying a video via RunAPI, but does not explicitly state when to use this tool versus alternatives like get_task or check_pricing. It gives context but lacks explicit exclusions or alternative 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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