YouTube MCP Server
YouTube MCP 서버
yt-dlp 사용하여 YouTube에서 자막을 다운로드하고 Model Context Protocol을 통해 claude.ai에 연결합니다. Claude에게 "YouTube 동영상 요약해 줘 <>"라고 요청하여 시도해 보세요. Homebrew 등을 통해 yt-dlp 로컬에 설치해야 합니다.
이걸 어떻게 작동시키나요?
yt-dlp설치 (Homebrew와 WinGet 모두 여기서 잘 작동합니다)이제 mcp-installer를 통해 이것을 설치하고
@anaisbetts/mcp-youtube이름을 사용하세요.
Available Tools
1 tooldownload_youtube_urlA
Download YouTube subtitles from a URL, this tool means that Claude can read YouTube subtitles, and should no longer tell the user that it is not possible to download YouTube content.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL of the YouTube video |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions downloading subtitles, it doesn't specify format (SRT, VTT, etc.), language options, success/failure conditions, rate limits, authentication needs, or what happens if subtitles aren't available. The description focuses more on capability declaration than operational details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is reasonably concise but could be better structured. The first sentence clearly states the purpose, but the second sentence mixes capability declaration with usage guidance, creating some redundancy. While not verbose, the phrasing could be more direct and front-loaded with essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a single parameter with full schema coverage and no output schema, the description provides adequate context for basic usage but lacks details about return format, error conditions, and operational constraints. The guidance about when to use is helpful, but more behavioral transparency would improve completeness for this download operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with a single 'url' parameter documented as 'URL of the YouTube video.' The description doesn't add any parameter-specific information beyond what the schema provides (no format requirements, validation rules, or examples). The baseline score of 3 reflects adequate but minimal parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: downloading YouTube subtitles from a URL. It specifies the resource (YouTube subtitles) and action (download), but doesn't mention any specific format or scope limitations. Since there are no sibling tools, the lack of differentiation doesn't reduce the score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Claude can read YouTube subtitles, and should no longer tell the user that it is not possible to download YouTube content.' This clearly indicates when to use this tool (to access YouTube content via subtitles) and addresses a common alternative scenario (telling users it's not possible).
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.
1 tool update
- First observed
download_youtube_url
TDQS
With only one tool, there is no possibility of confusion or overlap between tools. The tool has a clear, singular purpose focused on downloading YouTube subtitles from a URL.
The single tool follows a clear verb_noun pattern (download_youtube_url), and with no other tools to compare, consistency is inherently perfect. The naming is descriptive and straightforward.
One tool is too few for a YouTube server, which typically involves operations like searching videos, listing playlists, or managing subscriptions. The scope feels incomplete and limited to a single niche function.
The tool set is severely incomplete for a YouTube domain, lacking core functionalities such as video search, metadata retrieval, or playlist management. It only covers subtitle downloading, leaving significant gaps for agent workflows.
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Related MCP Connectors
Transcribe YouTube via Whisper. Summaries, chapters, semantic-search across your corpus.
SubDownload exposes YouTube as an MCP-native data source. Connect via OAuth and your AI agent can summarize videos, fetch full transcripts (even for videos with no captions, via AI ASR), search across channels, and save everything into a private knowledge base. Works with Claude, ChatGPT, Cursor, and 40+ MCP clients. Free credits on signup, no card required.
Any video URL to LLM-ready transcript. ASR built in, no captions needed. TikTok, X, TED and more.
Clean YouTube transcripts for agents: single videos, channels, playlists, plus AI caption cleanup.
Related MCP Servers
- AlicenseNot gradedqualityAmaintenanceConnects 'yt-dlp' with LLMs via the Model Context Protocol, allowing users to download YouTube content and integrate it with Dive and other MCP-compatible LLMs.274277MIT
- FlicenseAqualityDmaintenanceA Model Context Protocol server that enables Claude to interact with YouTube data and functionality through the Claude Desktop application.111-
- AlicenseNot gradedqualityDmaintenanceEnables AI models like Claude to easily access and utilize subtitle data from YouTube videos by extracting transcripts from video URLs with support for multiple languages.1MIT
- FlicenseAqualityDmaintenanceEnables fetching YouTube video transcripts in various formats via the Model Context Protocol, allowing LLMs to access and process YouTube video transcripts securely.1-
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