mcp-server-youtube-transcript
This server retrieves transcripts and subtitles from YouTube videos.
Extract video captions by providing a YouTube video URL or ID
Specify desired language for transcript retrieval (defaults to English)
Works with multiple video URL formats
Returns detailed metadata in responses
Includes robust error handling for invalid URLs, unavailable transcripts, and network issues
Can be integrated with Claude Desktop and other MCP-compatible applications
Enables retrieval of transcripts and subtitles from YouTube videos, supporting multiple video URL formats and language-specific transcript extraction.
Click on "Install 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., "@mcp-server-youtube-transcriptget transcript for https://youtu.be/ODaHJzOyVCQ with timestamps"
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 Transcript Server
A Model Context Protocol server that enables retrieval of transcripts from YouTube videos. This server provides direct access to video captions and subtitles through a simple interface.
Installing via Smithery
To install YouTube Transcript Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @kimtaeyoon83/mcp-server-youtube-transcript --client claudeComponents
Tools
get_transcript
Extract transcripts from YouTube videos
Inputs:
url(string, required): YouTube video URL, Shorts URL, or video IDlang(string, optional, default: "en"): Language code for transcript (e.g., 'ko', 'en'). Automatically falls back to available languages if requested language is not found.include_timestamps(boolean, optional, default: false): Include timestamps in output (e.g., '[0:05] text')strip_ads(boolean, optional, default: true): Filter out sponsorships, ads, and promotional content from transcript based on chapter markers
analyze_video (optional, requires a TwelveLabs API key)
Analyze a video with TwelveLabs Pegasus, a video-understanding model. Unlike
get_transcript, this reasons over what is shown on screen, so it produces useful summaries and answers even for videos with little or no speech (demos, gameplay, b-roll, music videos).Inputs:
url(string, required): A publicly reachable direct video URL (e.g. an.mp4/.mov/.webmlink or a pre-signed URL). TwelveLabs fetches the file server-side, so a YouTube watch page URL will not work — it serves HTML, not a raw video stream.prompt(string, optional): Instruction or question for the model (e.g."Summarize this video in 3 sentences"or"What products are shown?"). Defaults to a general summary.model(string, optional, default:"pegasus1.2"): Pegasus model to use ("pegasus1.2"or"pegasus1.5").max_tokens(number, optional, default: 2048): Maximum response length in tokens.
Requires the
TWELVELABS_API_KEYenvironment variable. The transcript tool works without it; analysis is fully opt-in. Grab a free key at twelvelabs.io — there's a generous free tier.
Related MCP server: YouTube Transcript Extractor MCP
Key Features
Support for multiple video URL formats (including YouTube Shorts)
Language-specific transcript retrieval with automatic fallback
Optional timestamps for referencing specific moments
Built-in ad/sponsorship filtering (enabled by default)
Zero external dependencies for transcript fetching
Detailed metadata in responses
Configuration
To use with Claude Desktop, add this server configuration:
{
"mcpServers": {
"youtube-transcript": {
"command": "npx",
"args": ["-y", "@kimtaeyoon83/mcp-server-youtube-transcript"]
}
}
}To enable the optional analyze_video tool, add your TwelveLabs API key:
{
"mcpServers": {
"youtube-transcript": {
"command": "npx",
"args": ["-y", "@kimtaeyoon83/mcp-server-youtube-transcript"],
"env": {
"TWELVELABS_API_KEY": "your-twelvelabs-api-key"
}
}
}
}Install via tool
mcp-get A command-line tool for installing and managing Model Context Protocol (MCP) servers.
npx @michaellatman/mcp-get@latest install @kimtaeyoon83/mcp-server-youtube-transcriptAwesome-mcp-servers
awesome-mcp-servers A curated list of awesome Model Context Protocol (MCP) servers.
Development
Prerequisites
Node.js 18 or higher
npm or yarn
Setup
Install dependencies:
npm installBuild the server:
npm run buildFor development with auto-rebuild:
npm run watchTesting
npm testDebugging
Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector for development:
npm run inspectorRunning evals
The evals package loads an mcp client that then runs the index.ts file, so there is no need to rebuild between tests. You can load environment variables by prefixing the npx command. Full documentation can be found here.
OPENAI_API_KEY=your-key npx mcp-eval src/evals/evals.ts src/index.tsError Handling
The server implements robust error handling for common scenarios:
Invalid video URLs or IDs
Unavailable transcripts
Language availability issues
Network errors
Usage Examples
Get transcript by video URL:
await server.callTool("get_transcript", {
url: "https://www.youtube.com/watch?v=VIDEO_ID",
lang: "en"
});Get transcript by video ID:
await server.callTool("get_transcript", {
url: "VIDEO_ID",
lang: "ko"
});Get transcript from YouTube Shorts:
await server.callTool("get_transcript", {
url: "https://www.youtube.com/shorts/VIDEO_ID"
});Get transcript with timestamps:
await server.callTool("get_transcript", {
url: "VIDEO_ID",
include_timestamps: true
});Get raw transcript without ad filtering:
await server.callTool("get_transcript", {
url: "VIDEO_ID",
strip_ads: false
});How to Extract YouTube Subtitles in Claude Desktop App
chat: https://youtu.be/ODaHJzOyVCQ?si=aXkJgso96Deri0aB Extract subtitlesSecurity Considerations
The server:
Validates all input parameters
Handles YouTube API errors gracefully
Implements timeouts for transcript retrieval
Provides detailed error messages for troubleshooting
License
This MCP server is licensed under the MIT License. See the LICENSE file for details.
Available Tools
1 toolget_transcriptARead-only
Extract transcript from a YouTube video URL or ID. Automatically falls back to available languages if requested language is not available.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | YouTube video URL or ID | |
| lang | No | Language code for transcript (e.g., 'ko', 'en'). Will fall back to available language if not found. | en |
| include_timestamps | No | Include timestamps in output (e.g., '[0:05] text'). Useful for referencing specific moments. Default: false | |
| strip_ads | No | Filter out sponsored segments from transcript based on chapter markers (e.g., chapters marked as 'Werbung', 'Ad', 'Sponsor'). Default: true |
Output Schema
| Name | Required | Description |
|---|---|---|
| meta | No | Title | Author | Subs | Views | Date |
| content | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and open-world hints, but the description adds valuable behavioral context: the automatic language fallback mechanism and the ad-stripping functionality based on chapter markers. This goes beyond annotations by explaining conditional behaviors and processing logic, though it doesn't cover rate limits or error handling.
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 a single, well-structured sentence that efficiently conveys the core functionality and key behavioral traits (language fallback). Every word serves a purpose, with no redundancy or unnecessary elaboration, making it easy to parse quickly.
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 the tool's moderate complexity (4 parameters, read-only operation) and the presence of both rich annotations and an output schema, the description is largely complete. It covers the main action and notable behaviors, though it could benefit from mentioning output format or error cases. The output schema likely handles return values, reducing the burden on the description.
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?
With 100% schema description coverage, the input schema fully documents all parameters. The description mentions language fallback and ad-stripping, which are already covered in the schema descriptions for 'lang' and 'strip_ads'. It adds no significant semantic information beyond what the schema provides, so the baseline score of 3 is appropriate.
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 specific action ('Extract transcript'), resource ('from a YouTube video'), and input type ('URL or ID'). It also mentions the fallback behavior for language selection, which adds specificity. With no sibling tools to distinguish from, this is maximally clear.
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 implies usage for extracting transcripts from YouTube videos, but provides no explicit guidance on when to use this tool versus alternatives (e.g., other transcript tools or manual methods). Since there are no sibling tools, it doesn't need to differentiate, but it lacks broader context about prerequisites or typical use cases.
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
v1.0.0- Added
get_transcript
TDQS
With only one tool, there is no possibility of ambiguity or overlap with other tools. The tool's purpose is clearly defined and distinct by default.
A single tool inherently has consistent naming, as there are no other tools to compare it against. The name 'get_transcript' follows a clear verb_noun pattern.
One tool is too few for a server named 'youtube-transcript', which suggests a broader domain. A complete surface might include tools for searching videos, listing transcripts, or handling metadata, making this feel thin and incomplete.
The server's purpose implies transcript-related operations, but with only a 'get' tool, there are significant gaps. For example, no tools for listing available transcripts, searching within transcripts, or managing transcript data, which limits agent workflows.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
An MCP server that gives any LLM or agent clean YouTube transcripts on demand: a single video, a whole channel, or a playlist, plus AI cleanup of auto-generated captions. API-key auth, credit-based, same backend as the public v1 API. Get a free API key with 25 free credits at youtubetranscriptdownload.com/account.
MCP server for RiverScript, an AI transcription platform - fetches transcripts shared via a link.
YouTube transcripts, search, channel/playlist listings and upload tracking for AI agents. No signup.
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