YouTube Data MCP Server

What is YouTube Data MCP Server about?

Provides a Model Context Protocol (MCP) server that exposes a suite of tools for accessing YouTube Data API v3. Each tool returns lean, JSON‑structured payloads designed to minimize token usage when fed to LLMs, covering video search, details, transcripts, channel stats, trending videos, categories, comments, and niche outlier analysis.

How to use YouTube Data MCP Server?

1. Obtain a YouTube Data API v3 key and (optionally) a MongoDB Atlas connection string for caching.
2. Add the server to an MCP‑compatible client (Claude Desktop, custom client, etc.) using the following configuration:

{
  "mcpServers": {
    "youtube": {
      "command": "npx",
      "args": ["-y", "@kirbah/mcp-youtube"],
      "env": {
        "YOUTUBE_API_KEY": "<YOUR_YOUTUBE_API_KEY>",
        "MDB_MCP_CONNECTION_STRING": "<YOUR_MONGODB_CONNECTION_STRING>"
      }
    }
  }
}

3. Run the client; the server will launch via npx -y @kirbah/mcp-youtube and listen for tool calls.
4. Invoke any of the provided tools through the client’s MCP interface, supplying the parameters defined in each tool’s schema.

Key Features

• Token‑efficient payloads: Only essential fields are returned, reducing LLM prompt size.
• Comprehensive YouTube toolkit: Video details, searchable video lists, multi‑language transcripts, channel statistics, top‑videos, trending videos, category listings, and comment extraction.
• MongoDB caching: Optional persistence layer to store API responses and analysis data, cutting down quota consumption.
• Strong validation: Zod schemas enforce correct input types and values.
• Fine‑grained cost awareness: Explicit quota unit costs per tool are documented.
• Outlier channel analysis: Advanced niche discovery requiring MongoDB.

Use Cases

• Automated content summarization: Feed video metadata and transcripts to an LLM for quick summaries.
• Market research: Pull channel statistics and trending videos to analyze competition.
• Sentiment analysis: Retrieve a controlled set of comments for LLM‑driven sentiment scoring.
• Idea generation: Search videos by keyword and get concise results to inspire new content.
• Niche scouting: Identify consistently high‑performing channels in a specific domain.

FAQ

Q: Do I need MongoDB? A: It is optional. Without it the server runs, but caching and the findConsistentOutlierChannels tool will be unavailable.

Q: How are API quota costs calculated? A: Most tools cost 1 unit per call; searchVideos costs 100 units, getChannelTopVideos 101 units, and comment replies add 1 unit per top‑level comment with replies.

Q: Which Node.js version is required? A: Node.js >= 20.0.0 as defined in package.json.

Q: Can I run the server locally for development? A: Yes. Clone the repo, run npm ci, set up a .env file with your keys, then npm run dev or npm start.

Q: How do I limit the number of results returned? A: Most tools accept maxResults (or similar) parameters; default values are defined in each tool’s schema.

YouTube Data MCP Server (@kirbah/mcp-youtube)

High-efficiency YouTube MCP server: Get token-optimized, structured data for your LLMs using the YouTube Data API v3.

This Model Context Protocol (MCP) server empowers AI language models to seamlessly interact with YouTube. It's engineered to return lean, structured data, significantly reducing token consumption and making it ideal for cost-effective and performant LLM applications. Access a comprehensive suite of tools for video search, detail retrieval, transcript fetching, channel analysis, and trend discovery—all optimized for AI.

Built with MCP TypeScript Starter

This project follows the architecture defined in the MCP TypeScript Starter. If you are looking to build your own MCP server using these same patterns (Class-based Tools, Dependency Injection, and strict Type Safety), I recommend using that repository as your starting point.

Quick Start: Adding to an MCP Client

The easiest way to use @kirbah/mcp-youtube is with an MCP-compatible client application (like Claude Desktop or a custom client).

1. Ensure you have a YouTube Data API v3 Key.

   • If you don't have one, follow the YouTube API Setup instructions below.

2. MongoDB Connection String (Optional): This server can use MongoDB to cache API responses and store analysis data, which significantly improves performance and reduces API quota usage. If you don't provide a connection string, the server will run without a database, but performance will be degraded, and you may hit API quota limits faster. You can get a free MongoDB Atlas cluster to obtain a connection string.

   Important: If you use MongoDB, the server is hardcoded to use the database name youtube_niche_analysis. Your connection string must point to this database, and your user must have read/write permissions for it.

3. Configure your MCP client: Add the following JSON configuration to your client, replacing "YOUR_YOUTUBE_API_KEY_HERE" with your actual API key.

   {
     "mcpServers": {
       "youtube": {
         "command": "npx",
         "args": ["-y", "@kirbah/mcp-youtube"],
         "env": {
           "YOUTUBE_API_KEY": "YOUR_YOUTUBE_API_KEY_HERE",
           "MDB_MCP_CONNECTION_STRING": "mongodb+srv://user:pass@cluster0.abc.mongodb.net/youtube_niche_analysis"
         }
       }
     }
   }
   

   • Windows PowerShell Users: npx can sometimes cause issues directly. If you encounter problems, try modifying the command as follows:

       "command": "cmd",
       "args": ["/k", "npx", "-y", "@kirbah/mcp-youtube"],
     

That's it! Your MCP client should now be able to leverage the YouTube tools provided by this server.

Why @kirbah/mcp-youtube?

In the world of Large Language Models, every token counts. @kirbah/mcp-youtube is designed from the ground up with this principle in mind:

• 🚀 Token Efficiency: Get just the data you need, precisely structured to minimize overhead for your LLM prompts and responses.
• 🧠 LLM-Centric Design: Tools and data formats are tailored for easy integration and consumption by AI models.
• 📊 Comprehensive YouTube Toolkit: Access a wide array of YouTube functionalities, from video details and transcripts to channel statistics and trending content.
• 🛡️ Robust & Reliable: Built with strong input validation (Zod) and clear error handling.

Key Features

• Optimized Video Information: Search videos with advanced filters. Retrieve detailed metadata, statistics (views, likes, etc.), and content details, all structured for minimal token footprint.
• Efficient Transcript Management: Fetch video captions/subtitles with multi-language support, perfect for content analysis by LLMs.
• Insightful Channel Analysis: Get concise channel statistics (subscribers, views, video count) and discover a channel's top-performing videos without data bloat.
• Lean Trend Discovery: Find trending videos by region and category, and get lists of available video categories, optimized for quick AI processing.
• Structured for AI: All responses are designed to be easily parsable and immediately useful for language models.
• Efficient Comment Retrieval: Fetch video comments with fine-grained control over the number of results and replies, optimized for sentiment analysis and feedback extraction.

Available Tools

The server provides the following MCP tools, each designed to return token-optimized data:

Tool Name	Description	Parameters (see details in tool schema)
getVideoDetails	Retrieves detailed, lean information for multiple YouTube videos including metadata, statistics, engagement ratios, and content details.	videoIds (array of strings)
searchVideos	Searches for videos or channels based on a query string with various filtering options, returning concise results.	query (string), maxResults (optional number), order (optional), type (optional), channelId (optional), etc.
getTranscripts	Retrieves token-efficient transcripts (captions) for multiple videos, with options for full text or key segments (intro/outro).	videoIds (array of strings), lang (optional string for language code), format (optional enum: 'full_text', 'key_segments' - default 'key_segments')
getChannelStatistics	Retrieves lean statistics for multiple channels (subscriber count, view count, video count, creation date).	channelIds (array of strings)
getChannelTopVideos	Retrieves a list of a channel's top-performing videos with lean details and engagement ratios.	channelId (string), maxResults (optional number)
getTrendingVideos	Retrieves a list of trending videos for a given region and optional category, with lean details and engagement ratios.	regionCode (optional string), categoryId (optional string), maxResults (optional number)
getVideoCategories	Retrieves available YouTube video categories (ID and title) for a specific region, providing essential data only.	regionCode (optional string)
getVideoComments	Retrieves comments for a YouTube video. Allows sorting, limiting results, and fetching a small number of replies per comment.	videoId (string), maxResults (optional number), order (optional), maxReplies (optional number), commentDetail (optional string)
findConsistentOutlierChannels	Identifies channels that consistently perform as outliers within a specific niche. Requires a MongoDB connection.	niche (string), minVideos (optional number), maxChannels (optional number)

For detailed input parameters and their descriptions, please refer to the inputSchema within each tool's configuration file in the src/tools/ directory (e.g., src/tools/video/getVideoDetails.ts).

Note on API Quota Costs: Most tools are highly efficient. getVideoDetails, getChannelStatistics, and getTrendingVideos cost only 1 unit per call. The getTranscripts tool has 0 API cost. The new getVideoComments tool has a variable cost: the base call is 1 unit, but if you request replies (by setting maxReplies > 0), it costs an additional 1 unit for each top-level comment it fetches replies for. The search-based tools are the most expensive: searchVideos costs 100 units and getChannelTopVideos costs 101 units.

Advanced Usage & Local Development

If you wish to contribute, modify the server, or run it locally outside of an MCP client's managed environment:

Prerequisites

• Node.js (version specified in package.json engines field - currently >=20.0.0)
• npm (usually comes with Node.js)
• A YouTube Data API v3 Key (see YouTube API Setup)

Local Setup

1. Clone the repository:

   git clone https://github.com/kirbah/mcp-youtube.git
   cd mcp-youtube
   

2. Install dependencies:

   npm ci
   

3. Configure Environment: Create a .env file in the root by copying .env.example:

   cp .env.example .env
   

   Then, edit .env to add your YOUTUBE_API_KEY:

   YOUTUBE_API_KEY=your_youtube_api_key_here
   MDB_MCP_CONNECTION_STRING=your_mongodb_connection_string_here
   

Development Scripts

# Run in development mode with live reloading
npm run dev

# Build for production
npm run build

# Run the production build (after npm run build)
npm start

# Lint files
npm run lint

# Run tests
npm run test
npm run test -- --coverage # To generate coverage reports

# Inspect MCP server using the Model Context Protocol Inspector
npm run inspector

Local Development with an MCP Client

To have an MCP client run your local development version (instead of the published NPM package):

1. Ensure you have a script in package.json for a non-watching start, e.g.:

   "scripts": {
     "start:client": "tsx ./src/index.ts"
   }
   

2. Configure your MCP client to spawn this local script:

   {
     "mcpServers": {
       "youtube_local_dev": {
         "command": "npm",
         "args": ["run", "start:client"],
         "working_directory": "/absolute/path/to/your/cloned/mcp-youtube",
         "env": {
           "YOUTUBE_API_KEY": "YOUR_LOCAL_DEV_API_KEY_HERE"
         }
       }
     }
   }
   

   Note on the env block above: Setting YOUTUBE_API_KEY directly in the env block for the client configuration is one way to provide the API key. Alternatively, if your server correctly loads its .env file based on the working_directory, you might not need to specify it in the client's env block, as long as your local .env file in the project root contains the YOUTUBE_API_KEY. The working_directory path must be absolute and correct for the server to find its .env file.

YouTube API Setup

1. Go to the Google Cloud Console.
2. Create a new project or select an existing one.
3. In the navigation menu, go to "APIs & Services" > "Library".
4. Search for "YouTube Data API v3" and Enable it for your project.
5. Go to "APIs & Services" > "Credentials".
6. Click "+ CREATE CREDENTIALS" and choose "API key".
7. Copy the generated API key. This is your YOUTUBE_API_KEY.
8. Important Security Step: Restrict your API key to prevent unauthorized use. Click on the API key name, and under "API restrictions," select "Restrict key" and choose "YouTube Data API v3." You can also add "Application restrictions" (e.g., IP addresses) if applicable.

System Requirements

• Node.js: >=20.0.0 (as specified in package.json)
• npm (for managing dependencies and running scripts)

Deep Dive: findConsistentOutlierChannels Tool

The findConsistentOutlierChannels tool is designed to identify emerging or established YouTube channels that consistently outperform their size within a specific niche. This tool is particularly useful for content creators, marketers, and analysts looking for high-potential channels.

Important Note: This tool requires a MongoDB connection to store and analyze channel data. Without MDB_MCP_CONNECTION_STRING configured, this tool will not be available.

Internal Logic Overview

The tool operates through a multi-phase analysis process, leveraging both YouTube Data API and a MongoDB database:

1. Candidate Search (Phase 1):

   • Uses the provided query to search for relevant videos and channels on YouTube.
   • Filters initial results based on videoCategoryId and regionCode if specified.
   • Collects a broad set of potential channels for deeper analysis.

2. Channel Filtering (Phase 2):

   • Retrieves detailed statistics for candidate channels (subscribers, total views, video count).
   • Filters channels based on channelAge (e.g., 'NEW' for channels under 6 months, 'ESTABLISHED' for 6-24 months).
   • Ensures channels meet a minimum video count to be considered for consistency.

3. Deep Analysis (Phase 3):

   • For each filtered channel, fetches their recent top-performing videos.
   • Calculates a "viral factor" for each video (e.g., views relative to subscriber count).
   • Assesses the consistencyLevel (e.g., 'MODERATE' for ~30% of videos showing outlier performance, 'HIGH' for ~50%).
   • Determines outlierMagnitude (e.g., 'STANDARD' for views > subscribers, 'STRONG' for views > 3x subscribers).

4. Ranking & Formatting (Phase 4):

   • Ranks channels based on their consistency, outlier magnitude, and overall performance within the niche.
   • Formats the results into a token-optimized structure suitable for LLMs, including key channel metrics and examples of outlier videos.

Key Parameters Controlling the Flow

The behavior of this tool is primarily controlled by the following parameters:

• query (string, required): The central topic or niche to analyze (e.g., "DIY home repair", "quantum computing explained").
• channelAge (enum: "NEW", "ESTABLISHED", default: "NEW"): Focuses the search on emerging or more mature channels.
• consistencyLevel (enum: "MODERATE", "HIGH", default: "MODERATE"): Sets the threshold for how consistently a channel's videos must perform as outliers.
• outlierMagnitude (enum: "STANDARD", "STRONG", default: "STANDARD"): Defines how significantly a video's performance must exceed typical expectations (e.g., views vs. subscribers) to be considered an "outlier."
• videoCategoryId (string, optional): Narrows the search to a specific YouTube category ID.
• regionCode (string, optional): Targets channels relevant to a particular geographical region.
• maxResults (number, default: 10): Limits the number of top outlier channels returned.

Security Considerations

• API Key Security: Your YOUTUBE_API_KEY is sensitive. Never commit it directly to your repository. Use environment variables (e.g., via a .env file which should be listed in .gitignore).
• API Quotas: The YouTube Data API has a daily usage quota (default is 10,000 units). All tool calls deduct from this quota. Monitor your usage in the Google Cloud Console and be mindful of the cost of each tool. For a detailed breakdown of costs per API method, see the official documentation.
• Input Validation: The server uses Zod for robust input validation for all tool parameters, enhancing security and reliability.

License

This project is licensed under the MIT License. See the LICENSE file for details.