YouTube Knowledge MCP
Provides AI-enhanced video processing features such as content analysis, learning path creation, knowledge graph generation, and transcript processing using OpenAI's language models
Offers optional caching functionality to reduce API costs and improve performance by storing frequently accessed YouTube data and AI analysis results
Provides comprehensive YouTube integration with tools for searching videos, fetching video details and transcripts, analyzing trending content, searching channels, and AI-powered features like content analysis, learning path generation, and knowledge graph creation
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@YouTube Knowledge MCP search for beginner Python tutorials from the last year"
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.
YouTube Knowledge MCP
Production-ready Model Context Protocol (MCP) server that turns YouTube into a queryable knowledge source. Search, fetch details, analyze transcripts/comments, and power AI workflows with optional LLMs. Built for Claude Desktop and other MCP clients.
Why this is special
Fast + quota-aware YouTube API access with caching
Batteries-included tools for search, details, trending, channels
Optional AI superpowers (OpenAI/Anthropic) for summaries, topics, chapters, learning paths, comment intents, and knowledge graphs
Zero noise: minimal config, clear logs, safe defaults
Requirements
Node.js 18+
YouTube Data API v3 key
Optional: OpenAI and/or Anthropic API keys for AI tools
Install
npm installConfigure environment
Create .env (or set variables in your MCP client config). You can start from the example:
cp env.example .envThen set values in .env:
# Required
YOUTUBE_API_KEY=your_youtube_api_key
# Optional AI providers (enables AI tools: analyze_video_content, generate_learning_path, analyze_comment_intents, simplify_video_transcript, generate_video_chapters, generate_knowledge_graph)
OPENAI_API_KEY=your_openai_api_key
ANTHROPIC_API_KEY=your_anthropic_api_key
# Optional tuning
LOG_LEVEL=info
MAX_DAILY_QUOTA=8000
REDIS_URL= # e.g. redis://localhost:6379
REDIS_HOST=
REDIS_PORT=
REDIS_PASSWORD=An env.example with placeholders is provided. Do not commit your .env.
Build and run
# Development (watch)
npm run dev
# Production
npm run build
npm startConnect to Claude Desktop (example)
Add to your Claude Desktop configuration with absolute paths:
{
"mcpServers": {
"youtube-knowledge": {
"command": "node",
"args": ["/absolute/path/to/youtube-knowledge-mcp/build/index.js"],
"env": {
"YOUTUBE_API_KEY": "your_youtube_api_key",
"OPENAI_API_KEY": "optional_openai",
"ANTHROPIC_API_KEY": "optional_anthropic",
"LOG_LEVEL": "info"
}
}
}
}Restart Claude Desktop after editing the config.
Available tools
youtube_search— Search videos with filtersget_video_details— Video metadata, transcript (best-effort), commentsget_trending_videos— Most popular by region/categorysearch_channels— Channel search with optional statsanalyze_video_content— AI topics/sentiment/questions/summary/keywordsgenerate_learning_path— AI learning path for a topicanalyze_comment_intents— Classify viewer intentssimplify_video_transcript— ELI5-style simplificationgenerate_video_chapters— AI chapters with timestampsgenerate_knowledge_graph— Cross-video concept graph
Note: AI tools are available only if an AI provider key is configured.
Quotas and safety
Enforces daily quota (default 8000 units) and cost-aware AI usage
Logs to stderr (does not break MCP stdio)
Caching reduces API and token spend; optional Redis supported
Troubleshooting
Missing key: ensure
YOUTUBE_API_KEYis setQuota exceeded: lower usage, enable caching, or raise
MAX_DAILY_QUOTAClaude cannot connect: verify absolute path to
build/index.jsand restart
License
MIT By Efi Kuta
Available Tools
10 toolsanalyze_comment_intentsC
Analyze YouTube comments to extract user intents and actionable insights
Inputs: videoId (required), maxComments, intentCategories.
analyze_video_contentC
Get AI-powered analysis and insights from video content
Inputs: videoId (required), analysisType, includeComments.
generate_knowledge_graphC
Create cross-video knowledge graphs showing concept relationships
Inputs: videoIds (required), graphDepth, focusTopics, includeTranscripts.
generate_learning_pathC
Generate AI-powered learning paths from YouTube content with difficulty assessment
Inputs: query (required), targetLevel, maxVideos, includeQuizzes.
generate_video_chaptersB
Generate AI-powered video chapters with timestamps and descriptions
Inputs: videoId (required), maxChapters, minChapterLength, includeDescriptions.
get_trending_videosC
Discover trending videos in different categories and regions
Inputs: category, region, maxResults.
get_video_detailsB
Get comprehensive information about a specific YouTube video
Inputs: videoId (required), includeTranscript, includeComments, maxComments, +1 more.
search_channelsC
Find and analyze YouTube channels
Inputs: query (required), maxResults, includeStats, order.
simplify_video_transcriptB
Create age-appropriate simplified versions of video transcripts (ELI5 mode)
Inputs: videoId (required), targetAge, outputFormat, includeDefinitions.
youtube_searchC
Search for videos on YouTube with advanced filtering options
Inputs: query (required), maxResults, publishedAfter, publishedBefore, +4 more.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 10 tools
Each tool has a clearly distinct purpose targeting specific YouTube operations: content analysis, search, metadata retrieval, and content transformation. There is no overlap in functionality; for example, analyze_video_content focuses on video insights while simplify_video_transcript handles transcript simplification, making misselection unlikely.
The naming follows a consistent verb_noun pattern with snake_case throughout, such as analyze_comment_intents and generate_video_chapters. The only minor deviation is youtube_search, which uses the platform name as a prefix instead of a verb, but it still fits the overall readable convention.
With 10 tools, the count is well-scoped for a YouTube knowledge server, covering a broad range of functionalities from search and analysis to content generation. Each tool earns its place by addressing distinct aspects of video and channel processing without feeling excessive or insufficient.
The tool set provides comprehensive coverage for YouTube knowledge extraction, including search, analysis, metadata retrieval, and content transformation. Minor gaps exist, such as the lack of tools for managing playlists or user interactions, but core workflows for learning and insights are well-supported.
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