YouTube MCP Server

Production-grade Model Context Protocol (MCP) server for YouTube intelligence. Extract, analyze, and automate insights from YouTube channels and videos using the official YouTube Data API v3.

Built for AI agents, automation pipelines, and content intelligence workflows.

What This Does

This MCP server exposes 16 specialized tools for YouTube analytics and automation:

• Channel Intelligence — subscriber counts, video lists, upload patterns, topic analysis

• Video Analytics — detailed metadata, engagement metrics, performance comparison

• Content Optimization — SEO scoring, tag analysis, thumbnail evaluation

• Audience Insights — comment extraction, keyword analysis, sentiment signals

• Trend Discovery — trending video tracking, competitive benchmarking

Built with zero scraping — uses only official YouTube Data API v3 endpoints for reliability and quota efficiency.

Prerequisites

YouTube Data API Key

You must have a YouTube Data API v3 key to use this server.

Get your API key:

1. Go to Google Cloud Console

2. Create a new project (or select existing)

3. Enable YouTube Data API v3:

   • Navigate to APIs & Services → Library

   • Search for "YouTube Data API v3"

   • Click Enable

4. Create credentials:

   • Go to APIs & Services → Credentials

   • Click Create Credentials → API Key

   • Copy your API key

Quota: Free tier provides 10,000 units/day (sufficient for ~100-500 tool calls depending on complexity).

Installation

Step 1: Clone the repository

Step 2: Install dependencies

Dependencies installed:

• mcp — MCP protocol server framework

• requests — YouTube Data API v3 HTTP client

• youtube-transcript-api — transcript fetching (no OAuth)

• Pillow — thumbnail image analysis

• nltk — natural language processing for comment keywords

• python-dotenv — environment variable management

Step 3: Configure API key

Create a .env file in the project root:

Important: Never commit .env to git. It's already in .gitignore.

Running the Server

Standalone (for testing)

With Claude Desktop

Add to your claude_desktop_config.json:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

Replace /absolute/path/to/Youtube-MCP/ with your actual project path.

Then: Fully quit and relaunch Claude Desktop.

Tools Reference (16 Total)

Channel Analysis

get_channel_overview

Subscriber count, total views, video count, creation date

get_channel_videos

Recent video list with views, likes, comments, duration

get_channel_topics

YouTube's topic categories (Gaming, Music, Tech, etc.)

compare_channels

Side-by-side channel comparison (max 5 channels)

get_upload_schedule

Posting frequency by day/hour, consistency score

Video Intelligence

get_video_details

Full metadata: title, description, tags, stats

get_video_comments

Top comments sorted by relevance with like counts

get_video_transcript

Auto-generated or manual transcripts for NLP

compare_videos

Performance comparison across multiple videos

get_top_videos

Channel's best performers by views/likes/engagement

SEO & Optimization

analyze_thumbnail

Thumbnail URL, resolution, file size metadata

get_video_seo_score

0-100 score: title, description, tags, thumbnail quality

get_tag_analysis

Tag frequency and correlation with video performance

get_trending_videos

Popular videos by region/category (Music, Gaming, Movies)

Advanced Analytics

get_engagement_stats

Like rate, comment rate, engagement rate per video

get_comment_keywords

Most frequent words in comments (stopwords filtered)

Architecture

Design principles:

• MCP = Data layer — deterministic, normalized JSON outputs

• AI = Reasoning layer — interprets data, generates insights

• No scraping — official API only for reliability

• Quota efficient — uses playlistItems.list instead of expensive search.list

Project Structure

Example Use Cases

AI Agents

• Automated YouTube research assistants

• Content strategy recommendation systems

• Competitive intelligence bots

Analytics Pipelines

• Channel performance dashboards

• Engagement trend tracking

• Audience sentiment analysis

Content Optimization

• SEO audit automation

• Tag optimization recommendations

• Upload schedule optimization

Research

• YouTube ecosystem analysis

• Creator behavior patterns

• Viral content dynamics

Contributing

Contributions welcome. Focus areas:

• Tool reliability — edge case handling, error messages

• Performance — quota optimization, caching strategies

• New tools — playlist analytics, growth metrics, predictions

• Documentation — usage examples, best practices

To contribute:

1. Fork the repository

2. Create a feature branch (git checkout -b feature/new-tool)

3. Commit changes (git commit -m 'Add new tool: X')

4. Push to branch (git push origin feature/new-tool)

5. Open a Pull Request

License

MIT License — see LICENSE file for details.

Author

Yash Kashte

GitHub: @Yashkashte5
Project: Youtube-MCP