YouTube Data MCP server

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

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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.

Why Choose This Server?

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

🎯 Want feedback on your own channel, not just raw data? Check out

CreatorLens - a companion Claude

Skill built on this MCP that diagnoses common growth problems (weak

hooks, bad thumbnails, flatlined videos) using a real strategist's

framework, not just numbers.

📉 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]

(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.

Shortened. The full README is on GitHub.