oriiion

Caption Video

process_video

Add AI-generated burned-in subtitles to a talking video (public URL). Uses the user's saved caption style and brand color unless overridden. The captioned video appears in the user's Todo list when ready (1-3 minutes).

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": {
      "videoUrl": "<videoUrl>"
    },
    "name": "process_video"
  }
}

Input Schema

Name	Required	Description	Default
`prompt`	No	The customer's original creative brief and constraints.
`editMode`	No	full retains every source section (default); highlights selects and shortens only when requested.	full
`videoUrl`	Yes	Public HTTPS URL of the video to caption
`silenceMode`	No	auto permits dead-air cleanup; off preserves source timing and pauses.
`captionStyle`	No	Caption style override. Defaults to the user's saved preference.
`targetPlatforms`	No	Selected channels; omit for eligible connected channels, empty for draft only.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 21, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already disclose write/non-idempotent/open-world behavior. The description adds real value beyond them: async completion via the Todo list and a 1-3 minute latency, plus the default-style/brand-color fallback behavior. It doesn't cover failure modes or rate limits, keeping it from a 5.

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 tight sentences with the core action front-loaded, followed by defaults and the async outcome. No filler; every sentence carries distinct 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?

For a six-param processing tool with no output schema and full schema coverage, the description supplies the missing pieces an agent needs: input constraint (public URL), default behavior, and how/when the result arrives. Only minor gaps remain (e.g. error handling).

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 the schema already documents all six parameters, including enums and defaults. The description only adds the 'unless overridden' nuance for captionStyle and the public-URL constraint, which is baseline 3 territory when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a precise verb+resource combo ('Add AI-generated burned-in subtitles to a talking video') that is clearly distinct from video-creation siblings like create_post_from_video or render_video_shoot. It doesn't explicitly name an alternative tool, so it stops just short of a 5.

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 scoping details ('talking video', 'public URL', default style unless overridden) imply when the tool applies, but there is no explicit when-to-use/when-not guidance or named alternative (e.g. edit_caption for existing captions). Usage is inferred rather than directed.

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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