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MalikElate

YouTube Transcript Extractor MCP

by MalikElate
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TypeScript

Extractor de transcripciones de YouTube MCP 🎥

Un servidor de Protocolo de Contexto de Modelo (MCP) que permite a los asistentes de IA extraer transcripciones de vídeos de YouTube. Diseñada para integrarse con Cursor y Claude Desktop, esta herramienta permite a la IA analizar y trabajar directamente con el contenido de vídeo de YouTube.

Características

  • 🎯 Extrae transcripciones de cualquier vídeo público de YouTube

  • 🔌 Fácil integración con Cursor y Claude Desktop

  • 🚀 Creado con TypeScript para seguridad de tipos

  • 📦 Configuración e implementación sencillas

  • 🛠️ Basado en el Protocolo de Contexto Modelo

Related MCP server: YouTube Translate MCP

Prerrequisitos

  • Node.js (v16 o superior)

  • pnpm (recomendado) o npm

  • Una URL de video de YouTube para extraer transcripciones

Instalación

  1. Clonar el repositorio:

git clone https://github.com/yourusername/yt-mcp.git
cd yt-mcp

  1. Instalar dependencias:

pnpm install

  1. Construir el proyecto:

pnpm run build

Configuración

Para el cursor

  1. Abrir configuración del cursor

  2. Vaya a MCP → Agregar nuevo servidor MCP

  3. Configure con estos ajustes:

    • Nombre: youtube-transcript

    • Tipo: command

    • Comando: node /absolute/path/to/yt-mcp/build/index.js

Para Claude Desktop

Agregue esta configuración a su configuración de Claude Desktop:

{
  "mcpServers": {
    "youtube-transcript": {
      "command": "node",
      "args": ["/absolute/path/to/yt-mcp/build/index.js"]
    }
  }
}

Uso

Una vez configurada, la IA puede extraer transcripciones de vídeos de YouTube llamando a la herramienta con la URL del vídeo. Ejemplo:

// The AI will use this format internally
const transcript = await extractTranscript({
  input: "https://www.youtube.com/watch?v=VIDEO_ID"
});

Detalles técnicos

El servidor está construido utilizando:

Limitaciones

  • Solo funciona con vídeos públicos de YouTube.

  • Los videos deben tener subtítulos habilitados

  • Algunos videos pueden tener subtítulos generados automáticamente que podrían no ser 100% precisos.

Solución de problemas

Problemas comunes y soluciones:

  1. Error "No se puede encontrar la identificación del video"

    • Asegúrese de que la URL de YouTube esté completa y sea correcta

    • Comprueba si el vídeo es de acceso público

  2. Error "No hay transcripción disponible"

    • Verifique que el video tenga subtítulos habilitados

    • Pruebe con un video diferente para confirmar que la herramienta funciona

  3. Errores de compilación

    • Asegúrese de que todas las dependencias estén instaladas

    • Verifique la versión de Node.js (debe ser v16 o superior)

Contribuyendo

¡Agradecemos sus contribuciones! No dude en enviar una solicitud de incorporación de cambios. Para cambios importantes, primero abra una incidencia para comentar qué desea cambiar.

Licencia

Instituto Tecnológico de Massachusetts (MIT)

Available Tools

1 tool
youtube-transcript-extractorC

Extracts the transcript of a YouTube video.

ParametersJSON Schema
NameRequiredDescriptionDefault
inputYesa youtube video url

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Extracts' implies a read operation, it lacks details on permissions, rate limits, error handling, or output format. This leaves significant gaps in understanding the tool's behavior beyond its basic function.

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 a single, efficient sentence: 'Extracts the transcript of a YouTube video.' It is front-loaded with the core purpose and contains no unnecessary words, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no annotations and no output schema, the description is incomplete. It lacks information on behavioral traits (e.g., error cases, rate limits) and output details (e.g., transcript format, structure). While concise, it does not provide enough context for an agent to use the tool effectively beyond basic invocation.

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?

The input schema has 100% description coverage, with the parameter 'input' documented as 'a youtube video url.' The description does not add any meaning beyond this, such as URL format examples or validation rules. Given the high schema coverage, the baseline score of 3 is appropriate.

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?

The description clearly states the tool's purpose: 'Extracts the transcript of a YouTube video.' It specifies the verb ('Extracts') and resource ('transcript of a YouTube video'), making the function unambiguous. However, with no sibling tools mentioned, there's no opportunity to differentiate from alternatives, preventing a perfect score.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives, prerequisites, or limitations. It simply states what the tool does without context for its application, leaving the agent to infer usage scenarios independently.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool update
    • First observedyoutube-transcript-extractor
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.1/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is singular and clearly defined, so an agent cannot misselect among multiple options.

Naming Consistency5/5

The single tool name follows a clear verb-noun pattern (youtube-transcript-extractor), and with no other tools to compare, consistency is inherently perfect. There are no deviations or mixed conventions to evaluate.

Tool Count2/5

One tool is too few for a server that appears to handle YouTube transcript extraction, as it lacks complementary operations like listing videos, handling errors, or supporting multiple formats. This minimal scope may limit agent workflows and feels incomplete for the domain.

Completeness2/5

The server is severely incomplete for YouTube transcript extraction, as it only provides extraction without supporting operations like validation, search, or handling different transcript types. This creates significant gaps that could cause agent failures in real-world scenarios.

Maintenance

ActivityInactive
ResponsivenessNo issues

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