Xenition

Create a video clip

create_video

Use this when the user wants a short AI video clip made from a description. Starts the video and returns its id right away; generating takes a few minutes, and video_status reports when it is ready with the file link. The clip is saved to their Xenition library.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prompt": "<prompt>"
    },
    "name": "create_video"
  }
}

Input Schema

Name	Required	Description
`title`	No
`aspect`	No	'landscape' (16:9, default), 'portrait' (9:16) or 'square'
`prompt`	Yes	what happens in the clip, with camera and style — e.g. 'slow drone shot over a misty pine forest at sunrise'
`duration`	No	length in seconds (the model may cap it; short clips of 5–10 s work best)

Output Schema

Name	Required	Description	Default
`url`	No
`kind`	No
`error`	No
`title`	Yes
`status`	Yes
`openUrl`	No
`artifactId`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 3, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Annotations only declare the generic write/open-world/idempotent profile; the description adds the genuinely important behavioral facts: the call is asynchronous and returns an id immediately, generation takes minutes, video_status is the readiness signal with the file link, and the artifact persists in the user's Xenition library. That is exactly the async/latency/persistence context annotations cannot convey.

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, front-loaded with the trigger condition, then the async contract, then persistence. No redundancy and every sentence carries distinct information.

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?

An output schema exists so return-value detail is unnecessary; the description still supplies what matters for correct invocation — immediate return of the id, the multi-minute latency expectation, and the status-check path. Nothing an agent needs to call this correctly is missing.

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 coverage is 75%, with prompt, aspect and duration already documented in the schema, so the description's "made from a description" adds little syntax or format meaning. The undocumented title parameter is not clarified either, leaving the baseline 3 appropriate.

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 (create) plus resource (short AI video clip) and scopes it to generation from a text description, which implicitly separates it from edit_video and from video_status. The mention of video_status as the follow-up step lets an agent place this tool in the workflow without opening sibling schemas.

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?

"Use this when the user wants a short AI video clip made from a description" gives a clear triggering condition, and the second sentence tells the agent to poll video_status afterwards. It stops short of an explicit contrast with edit_video (editing an existing clip) or guidance about prompt quality, so it is clear context rather than full when/when-not coverage.

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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