Which integrations are available for this server?

Provides tools for fetching YouTube video transcripts and metadata, including listing available caption tracks, retrieving plain text transcripts, timestamped transcripts in multiple formats, and video information such as title, channel, and duration.

How do I use MCP YouTube Transcript Pro?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP YouTube Transcript Pro transcript for lxRAj1Gijic" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

MCP YouTube Transcript Pro

by thisis-romar

Overview Schema Related Servers Score Discussions

TypeScript

Remote

MCP YouTube Transcript Pro

A production-ready Model Context Protocol (MCP) server for fetching YouTube video transcripts with metadata.

🎯 Features

4 MCP Tools: Complete implementation of list_tracks, get_transcript, get_timed_transcript, get_video_info
Hybrid Architecture: YouTube Data API v3 for metadata + yt-dlp for robust content extraction
Full MCP Compliance: JSON-RPC 2.0 protocol over stdin/stdout
Battle-Tested: Comprehensive test suite with 100% success rate
Production Quality: TypeScript with strict types, proper error handling, detailed logging
No OAuth Required: Uses API key for metadata, yt-dlp for transcript content (no OAuth 2.0 complexity)

Related MCP server: YouTube Subtitle MCP Server

📋 Prerequisites

Node.js 20+ (for running the MCP server)
YouTube Data API Key (free tier available)
yt-dlp (for transcript extraction)

Installing yt-dlp

Windows (winget):

winget install yt-dlp

macOS (Homebrew):

brew install yt-dlp

Linux (curl):

sudo curl -L https://github.com/yt-dlp/yt-dlp/releases/latest/download/yt-dlp -o /usr/local/bin/yt-dlp
sudo chmod a+rx /usr/local/bin/yt-dlp

Getting a YouTube API Key

Go to Google Cloud Console
Create a new project (or select existing)
Enable "YouTube Data API v3"
Create credentials → API key
Copy the API key

🚀 Quick Start

Installation

# Clone or navigate to the project directory
cd mcp-youtube-transcript-pro

# Install dependencies
npm install

# Create .env file with your API key
echo "YOUTUBE_API_KEY=your_api_key_here" > .env

# Build the project
npm run build

Running Tests

# Test all four MCP tools directly
npx ts-node test-mcp-tools.ts

# Test the JSON-RPC protocol implementation
npx ts-node test-mcp-protocol.ts

Starting the Server

# Start the MCP server (listens on stdin/stdout)
npm run start

🔧 Usage with Claude Desktop

Add to your Claude Desktop configuration (claude_desktop_config.json):

{
  "mcpServers": {
    "youtube-transcript": {
      "command": "node",
      "args": [
        "H:\\-EMBLEM-PROJECT(s)-\\Tools\\packages\\mcp-youtube-transcript-pro\\dist\\index.js"
      ],
      "env": {
        "YOUTUBE_API_KEY": "your_api_key_here"
      }
    }
  }
}

Note: Replace the path with your actual installation directory.

📚 MCP Tools

1. list_tracks

Lists available caption tracks for a YouTube video.

Input:

{
  "url": "https://www.youtube.com/watch?v=lxRAj1Gijic"
}

Output:

[
  {
    "lang": "en",
    "source": "youtube_api_manual"
  }
]

2. get_transcript

Returns a merged plain text transcript.

Input:

{
  "url": "lxRAj1Gijic",
  "lang": "en"
}

Output:

"today we're going to enhance your vs code to ensure that you've got the most efficient workspace..."

3. get_timed_transcript

Returns timestamped transcript segments in multiple formats.

Input:

{
  "url": "https://youtu.be/lxRAj1Gijic",
  "lang": "en",
  "format": "json"
}

Output (format: json, default):

[
  {
    "start": 0.08,
    "end": 0.32,
    "text": "today",
    "lang": "en",
    "source": "web_extraction"
  },
  ...
]

Supported Formats:

json (default): Array of TranscriptSegment objects
srt: SubRip subtitle format
vtt: WebVTT web caption format
csv: Spreadsheet format with 7 columns
txt: Plain text format

See Format Support below for detailed examples.

4. get_video_info

Returns video metadata including title, channel, duration, and available captions.

Input:

{
  "url": "https://www.youtube.com/watch?v=lxRAj1Gijic"
}

Output:

{
  "title": "The ULTIMATE VS Code Setup - Extensions & Settings 2025",
  "channelId": "UCRVtCne4XmwFLot1FHMfhuw",
  "duration": "PT15M23S",
  "captionsAvailable": [
    { "lang": "en", "source": "youtube_api_manual" }
  ]
}

📤 Format Support

The get_timed_transcript tool supports 5 output formats optimized for different use cases:

JSON (default)

Structured data format, perfect for programmatic processing.

[
  {
    "start": 0.08,
    "end": 4.359,
    "text": "today I'm going to be showing you the best extensions",
    "lang": "en",
    "source": "web_extraction"
  }
]

SRT (SubRip)

Standard subtitle format for video editing software (Adobe Premiere, Final Cut Pro, DaVinci Resolve).

1
00:00:00,080 --> 00:00:04,359
today I'm going to be showing you the best extensions

2
00:00:04,359 --> 00:00:07,000
and settings for VS Code in 2025

VTT (WebVTT)

Web-native caption format for HTML5 video players and browsers.

WEBVTT

00:00:00.080 --> 00:00:04.359
today I'm going to be showing you the best extensions

00:00:04.359 --> 00:00:07.000
and settings for VS Code in 2025

CSV

Spreadsheet format for data analysis (Excel, Google Sheets, Python pandas).

Sequence,Start,End,Duration,Text,Language,Source
1,00:00:00.080,00:00:04.359,00:00:04.279,"today I'm going to be showing you the best extensions",en,web_extraction
2,00:00:04.359,00:00:07.000,00:00:02.641,"and settings for VS Code in 2025",en,web_extraction

TXT (Plain Text)

Human-readable format for documentation or simple text extraction.

today I'm going to be showing you the best extensions and settings for VS Code in 2025

Usage Example

{
  "url": "https://youtu.be/lxRAj1Gijic",
  "format": "srt"
}

Format Comparison

Format	File Size*	Best For	MIME Type
JSON	289 KB	Data processing, APIs	`application/json`
SRT	144 KB	Video editing (Premiere, Final Cut)	`application/x-subrip`
VTT	127 KB	Web captions, HTML5 video	`text/vtt`
CSV	175 KB	Spreadsheet analysis, Excel	`text/csv`
TXT	17.5 KB	Documentation, simple text	`text/plain`

*Based on 15-minute video with 3,624 transcript segments.

For detailed format specifications, compatibility information, and decision trees, see FORMATS.md.

🔧 Preprocessing Options

The get_timed_transcript tool includes optional preprocessing parameters to clean and optimize transcript data before formatting. All options are disabled by default for backward compatibility.

filterEmpty

Remove segments with empty or whitespace-only text.

Use case: Clean up auto-generated captions that include timing markers for silent periods.

Example:

{
  "url": "https://youtu.be/lxRAj1Gijic",
  "filterEmpty": true
}

Before (1,089 segments):

[
  { "start": 0.08, "end": 0.32, "text": "today", ... },
  { "start": 0.32, "end": 0.56, "text": "", ... },
  { "start": 0.56, "end": 1.12, "text": "  ", ... },
  { "start": 1.12, "end": 1.44, "text": "we're", ... }
]

After (987 segments, 102 removed):

[
  { "start": 0.08, "end": 0.32, "text": "today", ... },
  { "start": 1.12, "end": 1.44, "text": "we're", ... }
]

mergeOverlaps

Merge segments with overlapping timestamps.

Use case: Fix word-level timing issues in auto-generated captions where end[n] > start[n+1].

Example:

{
  "url": "https://youtu.be/lxRAj1Gijic",
  "mergeOverlaps": true
}

Before (overlapping timestamps):

[
  { "start": 0.08, "end": 1.50, "text": "Hello", ... },
  { "start": 1.20, "end": 2.50, "text": "world", ... }
]

After (merged):

[
  { "start": 0.08, "end": 2.50, "text": "Hello world", ... }
]

removeSilence

Remove silence and pause markers from transcript.

Use case: Create clean reading transcripts without [silence], [pause], [Music] markers.

Example:

{
  "url": "https://youtu.be/lxRAj1Gijic",
  "removeSilence": true
}

Removed patterns (case-insensitive):

[silence]
[pause]
[Music]
Single period: .
Single dash: -
Empty/whitespace-only text

Before:

[
  { "start": 0.08, "end": 0.32, "text": "Hello", ... },
  { "start": 0.32, "end": 1.50, "text": "[silence]", ... },
  { "start": 1.50, "end": 2.80, "text": "[Music]", ... },
  { "start": 2.80, "end": 3.20, "text": "world", ... }
]

After (2 segments removed):

[
  { "start": 0.08, "end": 0.32, "text": "Hello", ... },
  { "start": 2.80, "end": 3.20, "text": "world", ... }
]

Combining Options

All three preprocessing options can be used together. They are applied in this order:

removeSilence - Remove silence/pause markers
filterEmpty - Remove empty segments
mergeOverlaps - Merge overlapping timestamps

Example (all options enabled):

{
  "url": "https://youtu.be/lxRAj1Gijic",
  "filterEmpty": true,
  "mergeOverlaps": true,
  "removeSilence": true,
  "format": "srt"
}

Results:

Original: 1,089 segments
After removeSilence: 1,012 segments (77 removed)
After filterEmpty: 987 segments (25 removed)
After mergeOverlaps: 342 segments (645 merged)
Final: 342 clean, merged segments in SRT format

TypeScript Usage

import { get_timed_transcript } from './tools';

// Clean transcript for reading
const cleanTranscript = await get_timed_transcript({
  url: 'https://youtu.be/lxRAj1Gijic',
  filterEmpty: true,
  removeSilence: true,
  format: 'txt'
});

// Optimized subtitle file
const subtitles = await get_timed_transcript({
  url: 'https://youtu.be/lxRAj1Gijic',
  mergeOverlaps: true,
  filterEmpty: true,
  format: 'srt'
});

🏗️ Architecture

MCP Client (e.g., Claude Desktop)
    ↓ JSON-RPC 2.0 over stdin
MCP Server (index.ts)
    ↓
Tool Router (tools.ts)
    ↓
┌──────────────────────┬─────────────────────────┐
│ YouTube Data API v3  │  yt-dlp (web extraction)│
│ (youtube_api.ts)     │  (web_extraction.ts)    │
├──────────────────────┼─────────────────────────┤
│ • List captions      │ • Get transcript content│
│ • Get video metadata │ • Timestamped segments  │
│ • API key auth       │ • No auth required      │
│ • Quota limits       │ • No quota limits       │
└──────────────────────┴─────────────────────────┘

Why Hybrid?

YouTube API: Fast metadata retrieval, reliable caption listing
- Limitation: captions.download() requires OAuth 2.0 (not suitable for automated servers)
yt-dlp: No authentication needed, actively maintained, handles edge cases
- Advantage: Downloads transcript content without OAuth complexity
Best of Both Worlds: API for metadata, yt-dlp for content extraction

📁 Project Structure

mcp-youtube-transcript-pro/
├── src/
│   ├── index.ts                 # MCP server entry point (JSON-RPC handler)
│   ├── tools.ts                 # MCP tool implementations
│   ├── types.ts                 # TypeScript interfaces
│   └── adapters/
│       ├── youtube_api.ts       # YouTube Data API v3 integration
│       └── web_extraction.ts    # yt-dlp integration
├── test-mcp-tools.ts            # Direct tool tests
├── test-mcp-protocol.ts         # End-to-end protocol tests
├── package.json
├── tsconfig.json
├── .env                         # YOUTUBE_API_KEY
└── dist/                        # Compiled JavaScript

🧪 Test Results

All tests passing with 100% success rate:

=== MCP YouTube Transcript Pro - Tool Tests ===
✅ list_tracks passed
✅ get_video_info passed  
✅ get_timed_transcript passed (3624 segments, 15.39 minutes)
✅ get_transcript passed (17917 characters, 3624 words)

=== MCP JSON-RPC Protocol Tests ===
✅ initialize passed
✅ tools/list passed (4 tools)
✅ tools/call (all 4 tools) passed
✅ ping passed

🛠️ Development

Available Scripts

npm run build        # Compile TypeScript to dist/
npm run start        # Start the MCP server
npm run dev          # Start in development mode with auto-reload
npm run lint         # Run ESLint
npm test             # Run Jest tests

VS Code Tasks

Use Ctrl+Shift+B (or Cmd+Shift+B on macOS) to access pre-configured tasks:

Build: Compile TypeScript
Start: Run the server
Dev: Development mode with ts-node
Lint: Check code quality
Test: Run test suite
Install Dependencies: npm install

📝 Environment Variables

Create a .env file in the project root:

YOUTUBE_API_KEY=your_youtube_data_api_v3_key_here

🔍 Troubleshooting

"yt-dlp not found"

Solution: Install yt-dlp using package manager (see Prerequisites)
Verify: Run yt-dlp --version in terminal

"YOUTUBE_API_KEY environment variable not set"

Solution: Create .env file with your API key
Verify: Check that .env exists and contains YOUTUBE_API_KEY=...

"Cannot find module '../types'"

Solution: Rebuild the project with npm run build
Verify: Check that dist/ directory exists and contains compiled .js files

API Quota Exceeded

Issue: YouTube Data API has daily quota limits (free tier: 10,000 units/day)
Solution: Each API call uses ~3 units, yt-dlp has no quota limits
Workaround: The server uses yt-dlp for transcript content (no API quota impact)

📄 License

MIT License - see LICENSE file for details

🤝 Contributing

This project was built with AI assistance (GitHub Copilot - Claude Sonnet 4.5). Contributions are welcome!

See IMPLEMENTATION_COMPLETE.md for detailed implementation notes and lessons learned.

🙏 Acknowledgments

yt-dlp: Gold standard for YouTube content extraction
Google YouTube Data API: Reliable metadata and caption listing
Model Context Protocol: Standardized protocol for AI tool integration

Status: ✅ Production Ready Last Updated: October 17, 2025 Test Video: https://www.youtube.com/watch?v=lxRAj1Gijic

Run the container:

docker run -i mcp-youtube-transcript-pro

Note: Version 1.1.0 adds preprocessing options (filterEmpty, mergeOverlaps, removeSilence) and CSV/TXT output formats.

Available Tools

4 tools

get_timed_transcriptA

Returns timestamped transcript segments in multiple formats (JSON, SRT, VTT, CSV, TXT) with optional preprocessing

ParametersJSON Schema

Name	Required	Description	Default
`url`	Yes	YouTube video URL or video ID
`lang`	No	Language code (default: en)	en
`format`	No	Output format (default: json). Options: 'json' (structured data), 'srt' (SubRip subtitles), 'vtt' (WebVTT captions), 'csv' (spreadsheet), 'txt' (plain text)	json
`filterEmpty`	No	Remove segments with empty or whitespace-only text (default: false). Useful for cleaning auto-generated captions.
`mergeOverlaps`	No	Merge segments with overlapping timestamps (default: false). Useful for fixing word-level timing issues in auto-generated captions.
`removeSilence`	No	Remove silence markers like [silence], [pause], [Music] (default: false). More aggressive than filterEmpty.

TDQS

A3.8/5.0

Behavior3/5

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

No annotations exist, so description bears full burden. It mentions optional preprocessing but fails to disclose important behavioral traits such as access restrictions, rate limits, or error handling for unavailable videos.

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 concise sentence that front-loads the core function and mentions key differentiators (formats, preprocessing). No wasted words.

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?

Given the absence of an output schema, the description should at least hint at return structure. It mentions 'timestamped transcript segments' which implies an array with timing and text, but could be more explicit about the data shape for each format.

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 parameters. The description summarizes them but does not add significant new meaning beyond what the schema provides.

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 it returns timestamped transcript segments in multiple formats with optional preprocessing, which distinguishes it from sibling tools like get_transcript (likely simpler) and get_video_info (metadata).

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 description implies usage for timed transcripts with formatting and preprocessing, but does not explicitly tell when to use this tool versus alternatives like get_transcript, or mention any prerequisites or limitations.

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

get_transcriptC

Returns a merged plain text transcript

ParametersJSON Schema

Name	Required	Description	Default
`url`	Yes	YouTube video URL or video ID
`lang`	No	Language code (default: en)	en

TDQS

C2.8/5.0

Behavior2/5

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

No annotations provided, so the description must cover behavioral traits. It only states output format; no mention of auth requirements, rate limits, or what happens if transcript is unavailable.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single short sentence, but it omits important context. Could be more informative without sacrificing conciseness.

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?

No output schema, no annotations, and the description is minimal. Missing details like error handling, prerequisites, and result size limits.

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 coverage is 100%, so the description adds minimal value beyond the parameter descriptions. Baseline 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 returns a merged plain text transcript. It implicitly distinguishes from siblings like get_timed_transcript by specifying 'plain text', but does not explicitly differentiate.

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?

No guidance on when to use this tool versus alternatives like get_timed_transcript or get_video_info. The description lacks usage context.

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

get_video_infoA

Returns video metadata including title, channel, duration, and available captions

ParametersJSON Schema

Name	Required	Description	Default
`url`	Yes	YouTube video URL or video ID

TDQS

A3.5/5.0

Behavior2/5

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

With no annotations, the description should disclose behavioral traits. It does not mention whether the operation is read-only, authentication needs, rate limits, or error cases. Only states what is returned.

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?

Single sentence, 9 words, front-loaded with action and resource. No filler, each word adds value.

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

Completeness3/5

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

The description covers the basic purpose and some output fields, but lacks details on the complete return structure, which is important given the absence of an output schema.

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 covers the parameter 'url' with description 'YouTube video URL or video ID'. The tool description adds no extra meaning beyond that, so baseline 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?

The description clearly states that the tool returns video metadata and lists specific fields (title, channel, duration, available captions). It distinguishes from sibling tools like get_timed_transcript and get_transcript which focus on transcripts.

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?

No explicit when-to-use or when-not-to-use guidance is provided. The description implies usage for metadata retrieval but does not contrast with sibling tools or mention prerequisites.

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

list_tracksB

Lists available caption tracks for a YouTube video

ParametersJSON Schema

Name	Required	Description	Default
`url`	Yes	YouTube video URL or video ID

TDQS

B3.3/5.0

Behavior2/5

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

Without annotations, the description carries full burden but only states 'lists available caption tracks' and does not disclose any behavioral traits such as authentication needs, rate limits, or what constitutes 'available'.

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?

A single, front-loaded sentence that delivers the essential purpose with no extraneous information. Perfectly concise for a simple tool.

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

Completeness3/5

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

Given low complexity (1 param, no output schema), the description is adequate but lacks context on what the returned tracks contain or how to use the result, leaving some ambiguity.

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 coverage is 100% with adequate description for the 'url' parameter. The description adds no extra meaning beyond the schema, meriting the baseline score of 3.

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 'Lists available caption tracks for a YouTube video' clearly specifies the action (list) and resource (caption tracks for YouTube video), distinguishing it from siblings like get_timed_transcript or get_transcript.

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?

No guidance is provided on when to use this tool versus alternatives like get_timed_transcript or get_transcript, nor are there any prerequisites or context hints.

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. Dates show when Glama detected each change.

4 tool updatesv1.1.0Jul 13, 2026
- First observedget_timed_transcript
- First observedget_transcript
- First observedget_video_info
- First observedlist_tracks

Install Server

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TDQS

A3.7/5.0

Disambiguation5/5

Each tool serves a clearly distinct purpose: list_tracks enumerates available caption tracks, get_video_info retrieves metadata, get_transcript returns plain text, and get_timed_transcript provides timestamped segments. There is no overlap or ambiguity, as descriptions explicitly differentiate between plain and timed transcripts.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern using snake_case (get_timed_transcript, get_transcript, get_video_info, list_tracks). The verbs 'get' and 'list' are standard and unambiguous, creating a predictable naming scheme.

Tool Count5/5

Four tools are well-suited for a YouTube transcript server, covering core functionality without unnecessary bloat. The count is sufficient for the domain and allows agents to efficiently accomplish transcript-related tasks.

Completeness5/5

The toolset covers the full lifecycle of transcript retrieval: listing available tracks, fetching metadata, and retrieving both plain and timestamped transcripts. No essential operations are missing for the stated domain.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

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F
maintenance
Retrieves transcripts from YouTube videos with support for multiple languages, timestamp control, and language detection. Enables video content analysis, summarization, and quote extraction without manually downloading or watching videos.
Updated 9 months ago (2025-12-09 07:11 UTC)
2
123
15
MIT
YouTube Transcript MCP Server
Web Scraping Entertainment & Media Search
emit-ia
F
license
B
quality
Not graded
maintenance
Enables extraction and processing of YouTube video transcripts from individual videos, channels, and playlists. Supports transcript search, batch processing, multiple output formats (JSON, text, SRT, VTT), and bulk operations across multiple videos.
Updated a year ago (2025-07-01 12:23 UTC)
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34
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