YouTube Data API

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

Link to glama.ai

A production-grade YouTube Data MCP server engineered specifically for AI agents.

Unlike standard API wrappers that flood your LLM with redundant data, this server strips away YouTube's heavy payload bloat. It is designed to save you massive amounts of context window tokens, protect your daily API quotas via caching, and run reliably without breaking your workflows.

Why Choose This Server?

Most MCP servers are weekend projects. @kirbah/mcp-youtube is built for reliable, daily, cost-effective agentic workflows.

📉 1. Save Up to 87% on Tokens (and Context Window)

The raw YouTube API returns massive JSON payloads filled with nested eTags, redundant thumbnails, and localization data that LLMs don't need. This server structures the data to give your LLM exactly what it needs to reason, and nothing else.

%%{init: { "theme": "base", "themeVariables": { "xyChart": { "plotColorPalette": "#ef4444, #22c55e" } } } }%%
xychart-beta
    title "Token Consumption (Lower is Better)"
    x-axis ["getVideoDetails", "searchVideos", "getChannelStats"]
    y-axis "Context Tokens" 0 --> 1200
    bar "Raw YouTube API" [854, 1115, 673]
    bar "MCP-YouTube (Optimized)" [209, 402, 86]

API Method	Raw YouTube Tokens	MCP-YouTube Tokens	Token Savings	Data Size
getChannelStatistics	673	86	~87% Less	1.9 KB ➔ 0.2 KB
getVideoDetails	854	209	~75% Less	2.9 KB ➔ 0.6 KB
searchVideos	1115	402	~64% Less	3.4 KB ➔ 1.2 KB

(Curious? You can compare the raw API responses vs optimized outputs in the examples folder).

🛡️ 2. Protect Your API Quotas (Smart Caching)

The YouTube Data API has strict daily limits (10,000 quota units). If your LLM gets stuck in a loop or re-asks a question, standard servers will drain your API limit in minutes. This server includes an optional MongoDB caching layer. If your agent requests a video details or searches the same trending videos twice, the server serves it from the cache - costing you 0 API quota points.

🏗️ 3. Production-Grade & Actively Maintained

Tired of MCP tools crashing your AI client? This server is built to be a rock-solid dependency:

• 97% Test Coverage: Comprehensively unit-tested (check the Codecov badge).
• Zero Lint Errors/Warnings: Enforces strict, clean code (npm run lint passes 100%).
• Active Security: Automated Dependabot patching ensures underlying libraries are never left with known vulnerabilities.
• Strict Type Safety: Built using Zod validation and the robust MCP TypeScript Starter architecture.

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Quick Start: Installation

The easiest way to install this server is by clicking the "Add to Claude Desktop" (or other supported clients) button on Glama server page.

Manual Configuration

If you prefer to configure your MCP client manually (e.g., Claude Desktop or Cursor), add the following to your configuration file:

1. Get a YouTube Data API v3 Key (See Setup Instructions below).
2. (Highly Recommended) Get a free MongoDB Connection String to enable quota-saving caching.

{
  "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:[email protected]/youtube_niche_analysis"
      }
    }
  }
}

(Windows PowerShell Users: If npx fails, try using "command": "cmd" and "args": ["/k", "npx", "-y", "@kirbah/mcp-youtube"])

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

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README truncated. View full README on GitHub.