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OpenAI Videos Remix

openai-videos-remix
Read-only

Create a remix of an existing video by providing a new prompt. Returns the updated video job and optionally downloads assets.

Instructions

Create a remix video job from an existing video_id. Returns structuredContent with the OpenAI Video job object, and (optionally) downloaded assets as MCP content blocks (tool_result=resource_link|resource).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
video_idYesSource video job id to remix.
promptYesUpdated prompt that directs the remix (max 32K chars).
wait_for_completionNoIf true, poll the job until completed/failed (then optionally download assets). Default: true.
timeout_msNoMax time to wait for completion when wait_for_completion is true (default: 900000).
poll_interval_msNoPolling interval when wait_for_completion is true (default: 2000).
download_variantsNoWhich downloadable assets to fetch when completed (default: ['video']).
tool_resultNoControls content[] shape: 'resource_link' (default) emits ResourceLink items, 'resource' emits EmbeddedResource blocks with base64 blob.resource_link
Behavior1/5

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

The description claims to 'Create a remix video job', which is a write/mutation operation, contradicting the annotation readOnlyHint=true. This is a serious inconsistency (annotation_contradiction=true). The description also doesn't disclose potential side effects beyond creation, such as cost implications or state changes.

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?

Two sentences efficiently convey purpose and return format. The first sentence immediately states the core action, and the second explains the output. No extraneous information.

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?

The description covers the essential aspects: what the tool does, the source video, and the return structure. However, it omits details about the job object shape (no output schema), and the 'structuredContent' term is not explained. Despite this, it provides sufficient context for typical usage.

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?

All 7 parameters have descriptions in the input schema (coverage 100%), so the description adds minimal additional meaning. It references return format but does not elaborate on parameter specifics; baseline 3 is appropriate as schema already carries the burden.

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 remix video job'), the resource ('from an existing video_id'), and the result (returns job object and optionally assets). It effectively distinguishes from sibling tools like openai-videos-create or openai-videos-retrieve by specifying the remix operation on an existing video.

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 identifies the tool's purpose but does not provide explicit guidance on when to choose it over alternatives (e.g., openai-videos-create for new videos) or when not to use it. No prerequisites or conditions are stated, leaving the agent to infer from context.

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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