YouTube MCP Server

Youtube List Transcript Languages

youtube_list_transcript_languages

List all caption tracks for a YouTube video, showing language codes, auto-generated status, and available translations. Use it to pick a language before fetching a transcript.

Instructions

List the caption tracks available for a YouTube video.

Returns one entry per track: language, language_code, whether it is auto-generated (is_generated), and which languages it can be translated to. Use this before youtube_get_transcript when you need a specific language, or to tell the user which languages exist. Generated tracks are usually less accurate than uploaded ones.

Cached forever, costs no Data API quota. Fails cleanly when a video has captions disabled or no caption track at all — that is a normal outcome, not an outage.

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": {
      "video_id": "<video_id>"
    },
    "name": "youtube_list_transcript_languages"
  }
}

Input Schema

Name	Required	Description	Default
`video_id`	Yes

Output Schema

Name	Required	Description	Default
`tracks`	Yes
`video_id`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 4, 2026

TDQS

A4.7/5.0

Behavior5/5

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

No annotations exist, so the description carries the full burden and does: it discloses caching ('Cached forever, costs no Data API quota') and the failure mode ('Fails cleanly when a video has captions disabled or no caption track at all — that is a normal outcome, not an outage'). It also warns that generated tracks are less accurate than uploaded ones.

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?

Front-loads the purpose in the first sentence, then covers return shape, usage routing, and reliability constraints without waste. Every sentence earns its place.

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 the field-level return documentation is a bonus rather than a necessity, and the operational caveats (quota, caching, clean failure) round out everything an agent needs to call and interpret this tool correctly.

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?

One parameter at 0% schema description coverage, so the schema provides no help. The description implies the input is a video identifier ('for a YouTube video') but never states the expected format or how to obtain the ID. Minimum-viable rather than compensatory.

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?

Specific verb (List) plus resource (caption tracks available for a YouTube video), and it names the entry fields returned. An agent can distinguish this from youtube_get_transcript, which fetches a transcript rather than enumerating tracks.

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

Usage Guidelines5/5

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

Explicitly states when to use it: 'Use this before youtube_get_transcript when you need a specific language, or to tell the user which languages exist.' It names the alternative tool and the condition that selects it, leaving nothing to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server