by kirbah
Unlock deep YouTube analytics: search videos, track channel stats, explore trends, and analyze high-performing YouTubers
Connects to YouTube Data API v3 to search videos, get channel stats, analyze trending content, and extract transcripts. Optimized for AI agents with reduced token usage and built-in caching.
YouTube Data API is a community-built MCP server published by kirbah that provides AI assistants with tools and capabilities via the Model Context Protocol. Unlock deep YouTube analytics: search videos, track channel stats, explore trends, and analyze high-performing YouTubers It is categorized under other, analytics data.
You can install YouTube Data API in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
YouTube Data API is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
Useful MCP listing: YouTube Data API is the kind of server we cite when onboarding engineers to host + tool permissions.
YouTube Data API is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
I recommend YouTube Data API for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
According to our notes, YouTube Data API benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
We wired YouTube Data API into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Strong directory entry: YouTube Data API surfaces stars and publisher context so we could sanity-check maintenance before adopting.
Useful MCP listing: YouTube Data API is the kind of server we cite when onboarding engineers to host + tool permissions.
I recommend YouTube Data API for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
We evaluated YouTube Data API against two servers with overlapping tools; this profile had the clearer scope statement.
We wired YouTube Data API into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
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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.
Most MCP servers are weekend projects. @kirbah/mcp-youtube is built for reliable, daily, cost-effective agentic workflows.
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).
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.
Tired of MCP tools crashing your AI client? This server is built to be a rock-solid dependency:
npm run lint passes 100%).The easiest way to install this server is by clicking the "Add to Claude Desktop" (or other supported clients) button on Glama server page.
If you prefer to configure your MCP client manually (e.g., Claude Desktop or Cursor), add the following to your configuration file:
{
"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"])
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
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.