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YouTube MCP

YouTube MCP

by thenavidm
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

List transcript languages

list_transcript_languages
Read-onlyIdempotent

List all caption languages on a YouTube video and identify whether each is human-written or auto-generated, so you can pick accurate transcripts before fetching.

Instructions

List every caption language on a video and whether each was written by a human or auto-generated. Human captions are more accurate. Call this before get_transcript when the video may not be in English.

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": "<video>"
    },
    "name": "list_transcript_languages"
  }
}

Input Schema

NameRequiredDescriptionDefault
videoYesVideo id or any YouTube URL: watch, youtu.be, Shorts, embed or live.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv3.0.2
    • removedInput schema / $schema
      Removed value: -"http://json-schema.org/draft-07/schema#"
    • removedInput schema / additionalProperties
      Removed value: -false
  2. First observedv1.0.0

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already cover readOnly/idempotent/non-destructive, so the bar is lower, and the description still adds real value by disclosing that languages are tagged human vs auto-generated and that human captions are more accurate. It does not discuss rate limits or pagination, but for a simple read-only listing that is not a serious gap.

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 short sentences with no filler: what it returns, why it matters, and when to call it. The output shape and the trigger condition are both front-loaded.

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?

With no output schema, the description carries the return-value burden and does so by naming the two fields an agent gets (language, origin). For a single-parameter read-only listing tool, nothing needed to call it 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?

Only one parameter and the schema already documents it at 100% coverage, including accepted URL forms (watch, youtu.be, Shorts, embed, live). The description adds no syntax or format detail beyond that, so the baseline of 3 applies.

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 (List) and resource (caption languages on a video) plus the discriminating attribute (human vs auto-generated). An agent can distinguish this from the sibling get_transcript without opening either schema.

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?

Gives an explicit condition for calling it: 'Call this before get_transcript when the video may not be in English.' Names the sibling tool and the situation 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.

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