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tuitamogamer-gpt

youtube-mcp-server

List Caption Tracks

youtube_list_captions
Read-onlyIdempotent

List all caption tracks available for a YouTube video, showing language, kind, and status for each track.

Instructions

List all caption tracks available for a YouTube video.

Args

  • videoId (string, required): The YouTube video ID whose captions to list.

  • response_format ("markdown" | "json", default "markdown"): Output format.

Returns JSON shape:

{
  "videoId": "string",
  "items": [
    {
      "id": "string",
      "language": "string",
      "name": "string",
      "trackKind": "standard|asr|forced",
      "isDraft": boolean,
      "isAutoSynced": boolean,
      "status": "string"
    }
  ],
  "totalCount": number
}

Examples

  • List captions for video "dQw4w9WgXcQ": videoId="dQw4w9WgXcQ"

  • Get JSON output: videoId="dQw4w9WgXcQ", response_format="json"

Errors

  • 403: Forbidden — you can only list captions for videos on your own channel, or you lack the youtube.force-ssl scope.

  • 404: Video not found or not accessible with current credentials.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
videoIdYesThe YouTube video ID whose caption tracks to list.
response_formatNoOutput format: "markdown" for human-readable text, "json" for raw structured data.markdown
Behavior4/5

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

Annotations already indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds context about required scopes (youtube.force-ssl) and error scenarios, which goes beyond the annotations.

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 well-organized with clear sections for Args, Returns, Examples, and Errors. It is concise yet comprehensive, with no unnecessary 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?

Given the tool has only 2 parameters and no output schema, the description provides a complete JSON shape for returns, covers errors, and gives examples. This fully equips an AI agent to use the tool correctly.

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

Parameters4/5

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

Schema coverage is 100% with descriptions for both parameters. The description adds examples and explains the default for response_format, providing additional meaning beyond the 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 'List all caption tracks available for a YouTube video', providing a specific verb (list) and resource (caption tracks). This distinguishes it from sibling tools like download, upload, update, or delete caption.

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 includes arguments, examples, and error conditions (403 Forbidden, 404 Not Found) that guide when the tool can be used. It does not explicitly name alternative tools but provides enough context for correct usage.

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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