
dogfrogfog/youtube-analytics-mcp
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Youtube analytics MCP server
概要
A Model Context Protocol (MCP) server for YouTube Analytics data access with demographics and discovery tools, built with a scalable config-driven architecture. - : Get comprehensive channel overview, growth patterns, and vital signs - : Analyze individual video metrics, audience retention, and drop-off points - : Access age/gender breakdowns and geographic distribution data - : Understand traffic sources and search terms driving views - : Track likes, comments, shares, and viewer interaction patterns - : Identify exact moments where viewers drop off for content optimization - : Compare metrics between different time periods - : Research competitor channels and trending content This MCP server follows a that provides: - : Clear separation between tool definitions and implementation - : Easy to add new tools without modifying core server logic - : Standardized error handling and response formatting - : Clean, declarative configuration that serves as documentation To use this...
README
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YouTube Analytics MCP Server
A Model Context Protocol (MCP) server for YouTube Analytics data access with demographics and discovery tools, built with a scalable config-driven architecture.
Core Features
- Channel Analytics: Get comprehensive channel overview, growth patterns, and vital signs
- Video Performance: Analyze individual video metrics, audience retention, and drop-off points
- Viewer Demographics: Access age/gender breakdowns and geographic distribution data
- Discovery Insights: Understand traffic sources and search terms driving views
- Engagement Metrics: Track likes, comments, shares, and viewer interaction patterns
- Audience Retention: Identify exact moments where viewers drop off for content optimization
- Performance Comparison: Compare metrics between different time periods
- Public Channel Analysis: Research competitor channels and trending content
Core Architecture Principles
This MCP server follows a config-driven architecture that provides:
- Maintainability: Clear separation between tool definitions and implementation
- Scalability: Easy to add new tools without modifying core server logic
- Consistency: Standardized error handling and response formatting
- Readability: Clean, declarative configuration that serves as documentation
Setup
1. Google API Credentials
To use this YouTube Analytics MCP server, you need to set up Google API credentials:
- Go to the Google Cloud Console
- Create a new project or select an existing one
- Enable the YouTube Analytics API and YouTube Data API v3
- Go to “Credentials” and create a new OAuth 2.0 Client ID
- Download the credentials as JSON
- Save the file as
credentials.jsonin thesrc/auth/directory
Privacy Note: All data processing happens locally on your computer. Your credentials and analytics data never leave your machine - the server runs entirely locally and connects directly to Google’s APIs from your system.
2. Development
# Install dependencies
npm install
# Build the project
npm run build
# Run in development mode
npm run dev
# Inspect with MCP Inspector
npm run inspect
Architecture Overview
Project Structure
src/
├── index.ts # Main server entry point (config-driven)
├── tool-configs.ts # Central tool configuration aggregator
├── types.ts # TypeScript interfaces and types
├── auth/
│ ├── tool-configs.ts # Authentication tool configurations
│ └── ...
├── server/
│ ├── info-configs.ts # Server info tool configurations
│ └── ...
└── youtube/tools/
├── channel-configs.ts # Channel analysis tool configurations
├── health-configs.ts # Channel health tool configurations
├── audience-configs.ts # Audience demographics tool configurations
├── discovery-configs.ts # Traffic source tool configurations
インストール
This server does not publish a one-line install command.
Open the repository installation guide MCP 初心者?mcpskill.org のガイドから始めましょう →