YouTube MCP Server Comparison 2026: Which One Should You Use?

With over 40 YouTube-related MCP servers available in community directories as of January 2026, choosing the right one for your workflow can feel overwhelming. Whether you need to summarize videos with Claude, automate transcript extraction, or analyze channel performance, there is a YouTube MCP server designed for your use case.

Model Context Protocol (MCP) is an open standard that enables AI assistants to connect directly with external data sources through a standardized interface. For YouTube specifically, MCP servers let you ask Claude or Cursor to extract transcripts, analyze video content, and manage playlists without manual copy-pasting.

What You'll Learn

• Feature comparison of the 6 most popular YouTube MCP servers
• How to choose between yt-dlp-based and YouTube Data API-based approaches
• API quota management strategies to avoid the 10,000 unit daily limit
• Decision framework for selecting the right server for your use case
• Setup complexity ratings and maintenance status for each option

This guide compares the leading YouTube MCP servers side-by-side, with verified statistics from official GitHub repositories and practical recommendations for content researchers, automation engineers, and analytics users.

Why Do Developers Need a YouTube MCP Server?

While ChatGPT can summarize YouTube videos using transcripts through manual copy-pasting or browser extensions, MCP servers offer a more systematic approach for repeated video analysis workflows.

According to the anaisbetts/mcp-youtube repository documentation, the server "uses yt-dlp to download subtitles from YouTube and connects it to claude.ai via Model Context Protocol." This means you can simply ask Claude to "Summarize the YouTube video" followed by any URL, and the MCP server handles transcript extraction automatically.

The key advantages of using a YouTube MCP server include:

• Automation: Process multiple videos without manual transcript copying
• Integration: Direct connection between AI assistants and YouTube data
• Consistency: Standardized output format for downstream processing
• Efficiency: Reduced context switching between browser tabs and AI tools

However, MCP servers come with tradeoffs. As noted in a 2025 analysis of MCP server performance, "more servers mean more active tools bloating context, and while five servers work well together, too many can degrade performance or cause strange/unpredictable LLM behavior."

What Are the Two Main Approaches to YouTube MCP Servers?

YouTube MCP servers fall into two architectural categories: yt-dlp-based servers for transcript extraction and YouTube Data API-based servers for comprehensive metadata access.

yt-dlp-Based Servers

These servers use yt-dlp, an open-source video downloader, to extract subtitles and transcripts directly from YouTube. The approach bypasses the official API, which means:

• No API key required for basic transcript extraction
• No quota limits to worry about
• Simpler setup with fewer configuration steps
• Limited to subtitle/transcript data only

Examples include anaisbetts/mcp-youtube and jkawamoto/mcp-youtube-transcript.

YouTube Data API-Based Servers

These servers use the official YouTube Data API v3, which provides comprehensive access to video metadata, channel analytics, and playlist management. According to Google's official documentation, "the YouTube Data API has a default quota allocation of 10,000 units per day."

Key considerations for API-based servers:

• API key required from Google Cloud Console
• Quota management critical for heavy use
• Full metadata access including views, likes, comments
• Channel management capabilities for creators

Examples include ZubeidHendricks/youtube-mcp-server.

Key Finding: Search operations cost 100 units per request while list operations cost only 1 unit, according to Google's quota documentation. Choose your MCP server based on whether you need search functionality or can work with direct video URLs.

How Do the Top YouTube MCP Servers Compare?

Based on verified statistics from official GitHub repositories, here is a feature comparison of the six most popular YouTube MCP servers.

Server	Stars	Language	Approach	Transcript	Metadata	Analytics	Setup Complexity
anaisbetts/mcp-youtube	490	JavaScript/TypeScript	yt-dlp	Yes	No	No	2/5
jkawamoto/mcp-youtube-transcript	283	Python (96.4%)	yt-dlp	Yes	No	No	2/5
kimtaeyoon83/mcp-server-youtube-transcript	--	TypeScript	yt-dlp	Yes	No	No	2/5
ZubeidHendricks/youtube-mcp-server	--	TypeScript	YouTube API	Limited	Yes	Yes	4/5
DumplingAI/mcp-server-dumplingai	--	TypeScript	Third-party API	Yes	No	No	3/5

Note: GitHub star counts shown are snapshots from late 2025 to January 2026 and are subject to change.

anaisbetts/mcp-youtube

The most popular YouTube MCP server by GitHub stars (490 stars, 59 forks as of March 2025), this server focuses on simplicity. The v0.6.0 release from March 20, 2025 uses yt-dlp under the hood.

Best for: Content researchers who need quick video summarization without API setup.

Limitations: No metadata access, no analytics, no playlist management.

jkawamoto/mcp-youtube-transcript

This Python-based server (96.4% Python) offers advanced transcript handling. According to its documentation, "this server splits transcripts that exceed 50,000 characters, and if a transcript is split, the response will include a next_cursor." The latest release v0.5.7 was published January 4, 2026.

Best for: Processing long-form content like podcasts, lectures, and documentaries where transcripts exceed standard limits.

Unique features: Pagination for long transcripts, proxy support for enterprise environments.

kimtaeyoon83/mcp-server-youtube-transcript

This TypeScript server emphasizes language support and content filtering. Per the repository documentation, it "supports language code for transcript with automatic fallback to available languages if requested language is not found."

Best for: International content analysis across multiple languages.

Unique features: Built-in ad/sponsorship filtering enabled by default, YouTube Shorts support, optional timestamps.

ZubeidHendricks/youtube-mcp-server

For users who need full YouTube Data API access, this server provides comprehensive capabilities. As described in its repository, it is "a Model Context Protocol (MCP) server implementation for YouTube, enabling AI language models to interact with YouTube content through a standardized interface."

Best for: YouTube creators and marketers who need analytics and video management.

Setup requirements: YouTube Data API v3 key, Google Cloud Console project, proper OAuth scope configuration.

DumplingAI/mcp-server-dumplingai

This server takes a different approach by routing through the Dumpling AI service. According to its documentation, it includes a get-youtube-transcript tool with optional timestamps and supports language preferences.

Best for: Users who prefer managed services over self-hosted infrastructure.

Requirements: Dumpling AI API key (third-party service).

How Should You Manage YouTube API Quota?

If you choose a YouTube Data API-based server, quota management becomes essential. According to Google's official documentation, the default allocation is 10,000 units per day.

Operation	Cost (Units)	Daily Limit at Default Quota
search.list	100	100 searches
videos.list	1	10,000 video lookups
channels.list	1	10,000 channel lookups
playlistItems.list	1	10,000 playlist item retrievals
commentThreads.list	1	10,000 comment retrievals

As GetLate.dev explains, "the quotaExceeded error occurs when you've used up your daily 10,000 API units from Google."

Pro Tip: If your workflow primarily involves processing known video URLs rather than searching, you can perform 10,000 video lookups per day with the default quota. Avoid search operations (100 units each) unless absolutely necessary.

Quota Management Strategies

1. Batch operations: Collect video URLs and process them in bulk rather than making real-time requests
2. Cache results: Store API responses locally to avoid redundant requests
3. Use yt-dlp for transcripts: Reserve API quota for metadata that only the official API provides
4. Request quota increase: Google allows verified applications to request higher quotas through the Cloud Console

What Security Considerations Apply to YouTube MCP Servers?

For teams deploying YouTube MCP servers in production environments, security best practices are critical. According to CData's 2025 MCP best practices guide, "access control is the foundation for any production-ready MCP deployment. MCP servers should enforce strict authentication and authorization."

The same source notes that "modern MCP implementations now standardize on OAuth 2.1 for HTTP-based transports, replacing custom authentication methods and basic API keys as of 2025."

For production deployments, consider:

• Environment variables for API keys (never hardcode credentials)
• OAuth 2.1 for HTTP-based MCP transports
• Secrets management using dedicated services rather than plain configuration files
• Network isolation for servers that process sensitive content

For detailed guidance on deploying MCP servers to production, including Docker containerization and cloud hosting options, see our dedicated deployment guide.

Which YouTube MCP Server Should You Choose?

Use this decision framework based on your primary use case:

For Content Researchers

Recommended: anaisbetts/mcp-youtube or kimtaeyoon83/mcp-server-youtube-transcript

If your workflow involves summarizing videos, extracting key insights, or analyzing content for research purposes, yt-dlp-based servers provide the simplest path. No API keys, no quota limits, and minimal configuration.

Choose kimtaeyoon83 if you need:

• Multi-language transcript support
• Ad/sponsorship filtering
• YouTube Shorts compatibility

For Long-Form Content Processing

Recommended: jkawamoto/mcp-youtube-transcript

For podcasts, lectures, webinars, and other content exceeding 50,000 characters, the pagination support in this Python server handles long transcripts elegantly. According to its documentation, "if a transcript is split, the response will include a next_cursor" for seamless pagination.

Enterprise users will also appreciate the proxy support: the server "supports proxy configurations through Webshare or other proxy servers using environment variables."

For YouTube Creators and Marketers

Recommended: ZubeidHendricks/youtube-mcp-server

If you need channel analytics, video metadata, and management capabilities, an API-based server is required. This server uses "YouTube Data API v3 for comprehensive data access" and can be "installed via Smithery for automated setup."

Be prepared for:

• More complex setup (Google Cloud Console project, API key, OAuth configuration)
• Quota management requirements (10,000 units/day default)
• Potential troubleshooting of API scope configurations

For Managed Service Preference

Recommended: DumplingAI/mcp-server-dumplingai

If you prefer not to manage yt-dlp or API credentials directly, the Dumpling AI server routes requests through their managed infrastructure. This requires a Dumpling AI API key but simplifies deployment.

How Do You Set Up a YouTube MCP Server with Claude Desktop?

Here is a sample configuration for Claude Desktop using the popular anaisbetts/mcp-youtube server:

{
  "mcpServers": {
    "youtube": {
      "command": "npx",
      "args": ["-y", "@anthropic/mcp-youtube"]
    }
  }
}

For the Python-based jkawamoto/mcp-youtube-transcript:

{
  "mcpServers": {
    "youtube-transcript": {
      "command": "uvx",
      "args": ["mcp-youtube-transcript"]
    }
  }
}

For API-based servers like ZubeidHendricks/youtube-mcp-server, you will need to include environment variables:

{
  "mcpServers": {
    "youtube-api": {
      "command": "npx",
      "args": ["-y", "@zubeidhendricks/youtube-mcp-server"],
      "env": {
        "YOUTUBE_API_KEY": "your-api-key-here"
      }
    }
  }
}

Watch Out: Never commit API keys to version control. Use environment variables or secrets management for all credentials.

For common setup issues like "spawn npx ENOENT" errors, see our MCP troubleshooting guide.

What About Performance Considerations?

While specific benchmarks for YouTube MCP servers are limited, general MCP performance research provides useful context. According to a January 2026 benchmark study by AIMultiple, "a comprehensive benchmark tested 8 MCP servers across web search & extraction, and browser automation tasks, including a load test involving 250 concurrent MCP agents."

The study found significant variation in performance across MCP servers, with completion times ranging from 31.7 seconds to 182.3 seconds for similar operations. While these benchmarks focused on browser automation rather than YouTube-specific tasks, they highlight the importance of:

• Choosing servers actively maintained with recent releases
• Testing with your specific workload before production deployment
• Monitoring for context bloat when using multiple MCP servers simultaneously

For users comparing MCP vs traditional API integration, the convenience of MCP comes with some overhead, but for most YouTube analysis workflows, this tradeoff is worthwhile.

What Happens When You Use Multiple MCP Servers?

Many users want to combine YouTube MCP servers with other tools. For example, pairing a YouTube transcript server with our Google Search Console MCP servers creates a powerful content research workflow.

However, the previously cited warning applies: "five servers work well together, but too many can degrade performance or cause strange/unpredictable LLM behavior." For multi-server setups:

• Start with the minimum required for your workflow
• Monitor context usage in your AI tool of choice
• Disable unused servers in your MCP configuration when not needed
• Consider specialized servers that combine multiple capabilities

Frequently Asked Questions

Do I need a YouTube API key to use a YouTube MCP server?

It depends on the server you choose. yt-dlp-based servers like anaisbetts/mcp-youtube and jkawamoto/mcp-youtube-transcript do not require an API key because they extract transcripts directly from YouTube. API-based servers like ZubeidHendricks/youtube-mcp-server require a YouTube Data API v3 key from Google Cloud Console.

How many videos can I process per day with the YouTube Data API?

According to Google's official documentation, the default quota is 10,000 units per day. Since video.list operations cost 1 unit each, you can look up metadata for 10,000 videos daily. However, search operations cost 100 units each, limiting searches to 100 per day at the default quota.

Which YouTube MCP server is best for podcast transcripts?

For long-form content like podcasts, jkawamoto/mcp-youtube-transcript is recommended because it handles transcripts exceeding 50,000 characters with automatic pagination. The server returns a next_cursor for seamless continuation of long transcripts.

Can I use YouTube MCP servers with Cursor or other AI coding assistants?

Yes. MCP is a standard protocol supported by multiple AI tools including Claude Desktop, Cursor, and Windsurf. The configuration syntax may vary slightly between tools, but the underlying MCP servers work across compatible clients.

What causes 403 Forbidden errors with YouTube MCP servers?

For API-based servers, 403 errors typically indicate misconfigured API scopes in the Google Cloud Console. Ensure the YouTube Data API v3 is enabled and your OAuth consent screen includes the required scopes. For yt-dlp-based servers, 403 errors may indicate regional restrictions or rate limiting from YouTube.

How do I handle transcripts in multiple languages?

The kimtaeyoon83/mcp-server-youtube-transcript supports language-specific transcript retrieval with automatic fallback. According to its documentation, it "supports language code for transcript with automatic fallback to available languages if requested language is not found."

Should I use Docker to deploy my YouTube MCP server?

Docker provides isolation and reproducibility benefits for production deployments. Docker containerization is particularly useful for team environments where consistent configuration is important. For local development, direct installation via npx or uvx is often simpler.

Sources

1. anaisbetts (2025). "mcp-youtube: A Model-Context Protocol Server for YouTube." https://github.com/anaisbetts/mcp-youtube

2. jkawamoto (2026). "mcp-youtube-transcript: MCP server retrieving transcripts of YouTube videos." https://github.com/jkawamoto/mcp-youtube-transcript

3. kimtaeyoon83 (2025). "mcp-server-youtube-transcript." https://github.com/kimtaeyoon83/mcp-server-youtube-transcript

4. ZubeidHendricks (2025). "youtube-mcp-server: MCP Server for YouTube API." https://github.com/ZubeidHendricks/youtube-mcp-server

5. DumplingAI (2026). "mcp-server-dumplingai: MCP server for Dumpling AI." https://github.com/DumplingAI/mcp-server-dumplingai

6. Google Developers (2025). "Quota costs for API requests | YouTube Data API." https://developers.google.com/youtube/v3/determine_quota_cost

7. GetLate.dev (2025). "YouTube API Quota Exceeded? Here's How to Fix It." https://getlate.dev/blog/youtube-api-limits-how-to-calculate-api-usage-cost-and-fix-exceeded-api-quota

8. Plain English (2025). "5 Essential MCP Servers That Give Claude & Cursor Real Superpowers." https://plainenglish.io/blog/5-essential-mcp-servers-that-give-claude-cursor-real-superpowers-2025

9. CData (2025). "MCP Server Best Practices for 2026." https://www.cdata.com/blog/mcp-server-best-practices-2026

10. AIMultiple Research (2026). "MCP Benchmark: Top MCP Servers for Web Access in 2026." https://research.aimultiple.com/browser-mcp/