YouTube MCP Server: Connect AI Agents to YouTube Data

The official YouTube Data API is rate-limited, quota-capped, and missing the things agents actually need (transcripts, deep comment threads, channel-level analytics that mirror what creators see). A YouTube MCP server fills that gap. This post explains what an MCP server is, what tools a useful YouTube MCP exposes, and how to set up CreatorCrawl's in the major MCP clients.

#What an MCP server is

MCP (Model Context Protocol) is a protocol from Anthropic that lets AI agents discover and call tools on an external server. The client connects to the server, lists the available tools, then the agent picks which ones to call based on the prompt.

For YouTube specifically the protocol matters because YouTube data is hierarchical. An agent rarely wants just a channel or just a video. It wants to chain: pull the channel, pull the recent videos, fetch transcripts for the best performers, scan the comments. MCP lets the agent do that chain without orchestration code on your side.

#What CreatorCrawl's YouTube MCP exposes

Tool	Purpose
get_youtube_channel	Channel profile, subscriber count, total views, recent uploads
get_youtube_channel_videos	List videos for a channel with pagination
get_youtube_channel_shorts	List shorts for a channel
get_youtube_video	Single video metadata, views, likes, duration
get_youtube_comments	Comments on a video including replies
get_youtube_transcript	Full captions or auto-generated transcript
get_youtube_playlist	Playlist contents
search_youtube	Keyword search across YouTube
search_youtube_hashtag	Hashtag search across YouTube
get_youtube_trending_shorts	Currently trending shorts

Each tool returns structured JSON. The transcript tool returns plain text segments with timestamps, which is the input format LLMs need for content summarisation.

#Why MCP beats raw API integration for agents

Three concrete reasons.

You get a unified credit model. Each MCP call costs 1 credit, same as a REST call. No quota juggling, no separate auth flows per endpoint, no parts of YouTube unavailable through MCP.

You get tool discovery. The agent reads the tool list once and knows what is available. New endpoints we add show up automatically without your code changing.

You get composable calls. "Find the most-viewed video from this channel and summarise its transcript" is one prompt. With REST it is three function calls plus glue code. With MCP the agent figures out the chain.

#Setup

#Prerequisites

Sign up for CreatorCrawl, grab an API key.
Pick an MCP client.

Endpoint: https://app.creatorcrawl.com/api/mcp.

#Claude Desktop

Settings, MCP Servers, add this:

{
  "mcpServers": {
    "creatorcrawl": {
      "url": "https://app.creatorcrawl.com/api/mcp",
      "headers": {
        "x-api-key": "your_api_key_here"
      }
    }
  }
}

Restart Claude Desktop.

#Cursor

Cursor settings, MCP Servers, paste the same JSON.

#Windsurf

Windsurf Settings > MCP, same JSON, restart.

#Custom client

import { Client } from '@modelcontextprotocol/sdk/client/index.js'
import { StreamableHTTPClientTransport } from '@modelcontextprotocol/sdk/client/streamableHttp.js'

const transport = new StreamableHTTPClientTransport(
  new URL('https://app.creatorcrawl.com/api/mcp'),
  { requestInit: { headers: { 'x-api-key': 'your_api_key_here' } } },
)
const client = new Client({ name: 'youtube-agent', version: '1.0.0' })
await client.connect(transport)

const transcript = await client.callTool({
  name: 'get_youtube_transcript',
  arguments: { videoId: 'dQw4w9WgXcQ' },
})

#Example prompts

#Content research

"Find the top 10 YouTube videos under the query 'cold email best practices' from the last 12 months. Pull each transcript and tell me what tactics every video agrees on, and where they disagree."

The agent searches, fetches transcripts, runs a synthesis pass.

#Competitor analysis

"Pull the last 20 videos from the @mkbhd channel. Show me view count, like ratio, and how the title style has changed over time."

Channel call, channel-videos call, agent does the analysis.

#Creator due diligence for sponsorship

"Audit the channel @somecreator. Tell me their average view count, engagement rate, the typical sponsorship slots in their videos, and whether their comments feel organic. Pull a sample of comments from their three most-viewed videos."

Channel + videos + comments + reasoning, all in one prompt.

#Long-form summarisation

"Summarise this YouTube video [URL] into a structured brief: main argument, three to five key points, and the most quotable line."

Single transcript call, model does the rest.

#Trend mapping

"Search YouTube for videos about 'AI agents' from the last 6 months. Cluster them by topic and tell me which clusters have the most views."

Search call, video-info calls in parallel, model clusters.

#Pricing

Each tool call uses 1 credit:

Pack	Credits	Price
Free	250	$0
Starter	5,000	$29
Pro	20,000	$99
Scale	100,000	$299

Credits never expire. No subscription minimum.

#Notes on transcripts

The transcript tool returns captions if the creator uploaded them, or YouTube's auto-generated captions if not. Auto-generated transcripts are usually accurate enough for LLM consumption. The tool returns timestamped segments which lets the agent quote specific moments back to the user.

#FAQ

Is this the official YouTube Data API. No. The official API has quota limits that make agent workflows painful. CreatorCrawl returns the same data structured for agent use without the quota.

Does this work for private or unlisted videos. No. Public videos only.

Can I use this from non-Anthropic agents. Yes. MCP is open. OpenAI Responses API, LangChain agents, your own runtime, anything that speaks MCP works.

#Next

Read the MCP docs for the full tool catalog. Sign up to start. If you need TikTok, Instagram, Reddit, LinkedIn, or Twitter data alongside YouTube, the same endpoint covers all of them.