Youtube Analytics

YouTube Analytics

Analyze YouTube channels and videos using the YouTube Data API v3, generate a notebook report with visualizations, and deploy to Vercel.

Prerequisites

• YOUTUBE_API_KEY environment variable must be set (YouTube Data API v3 key from GCP)
• If not set, inform the user and stop

Step 1: Identify the Target

Ask or infer from the user's message:

• Channel: by name, handle (@handle), or channel ID
• Specific videos: by URL or title
• Analysis focus: general overview, trends, engagement, comparison, etc.

Step 2: Fetch Data via YouTube Data API v3

Use Python's urllib.request and json modules (no external HTTP libraries needed).

Base URL: https://www.googleapis.com/youtube/v3

Common endpoints:

Find Channel ID (if user gives name/handle)

import urllib.request, json, os
API_KEY = os.environ['YOUTUBE_API_KEY']

# Search for channel
url = f'https://www.googleapis.com/youtube/v3/search?part=snippet&type=channel&q={query}&key={API_KEY}'
data = json.loads(urllib.request.urlopen(url).read())
channel_id = data['items'][0]['snippet']['channelId']

Channel Statistics

url = f'https://www.googleapis.com/youtube/v3/channels?part=statistics,snippet,contentDetails&id={channel_id}&key={API_KEY}'

List Videos (via playlist — uploads playlist)

# Get uploads playlist ID from channel's contentDetails.relatedPlaylists.uploads
url = f'https://www.googleapis.com/youtube/v3/playlistItems?part=snippet,contentDetails&playlistId={uploads_playlist_id}&maxResults=50&key={API_KEY}'

Video Statistics (batch up to 50 IDs)

video_ids = ','.join(ids)
url = f'https://www.googleapis.com/youtube/v3/videos?part=statistics,snippet,contentDetails&id={video_ids}&key={API_KEY}'

Quota tips:

• YouTube Data API v3 has a daily quota of 10,000 units
• search.list costs 100 units; most other calls cost 1 unit
• Minimize search calls. Prefer channels.list and playlistItems.list
• Batch video IDs (up to 50 per request) to reduce calls
• For large channels, limit to the most recent 100-200 videos

Step 3: Create the Notebook

Use the NotebookEdit tool to create an .ipynb file. Set kernel to python3.

Recommended notebook structure:

1. Title & Overview (markdown): Channel name, subscriber count, total views, video count
2. Data Fetching (code): API calls to collect video data into a pandas DataFrame
3. Views Distribution (code+viz): Histogram or box plot of view counts
4. Top Videos (code+viz): Bar chart of top 10/20 videos by views
5. Engagement Analysis (code+viz): Like rate (likes/views), comment rate
6. Publishing Trends (code+viz): Videos per month/year, day-of-week patterns
7. Performance Over Time (code+viz): Views vs publish date scatter/line
8. Summary (markdown): Key findings and insights

Matplotlib setup: Always include this in the setup cell:

import matplotlib
matplotlib.rcParams['font.family'] = 'Noto Sans CJK JP'  # Japanese font support

Use the default matplotlib style (white background, black text). Do NOT use dark themes — they are hard to read in notebook HTML output.

Adapt the structure based on the user's specific request. For example:

• Channel comparison → side-by-side metrics
• Single video deep dive → engagement metrics, related videos
• Trend analysis → time series focus

Step 4: Execute the Notebook

jupyter nbconvert --to notebook --execute --inplace <notebook-file>.ipynb

Do NOT use jupyter execute as it may not persist outputs to the file.

If execution fails due to API errors:

1. Check if YOUTUBE_API_KEY is set
2. Check quota limits (may need to reduce data fetched)
3. Fix and retry up to 2 times

Step 5: Convert to HTML

jupyter nbconvert --to html --template classic <notebook-file>.ipynb

Always use --template classic for standalone HTML that renders correctly outside JupyterLab.

By default, code cells are shown (notebook style with In [n]: prompts). If the user explicitly asks to hide the code, add --no-input.

Step 6: Deploy to Vercel

Read the HTML file, then deploy:

mcp__vercel__vercel_deploy({
  name: "youtube-<channel-slug>",
  files: [
    { file: "index.html", data: "<the full HTML content>" }
  ]
})

Step 7: Return Result

Respond with:

1. The deployed URL
2. Key findings (subscriber count, average views, top video, etc.)
3. Any data limitations (quota, private videos, etc.)

Example:

YouTube分析レポートをデプロイしました！

URL: https://youtube-hikakin-xxx.vercel.app

HikakinTVの分析結果:
- チャンネル登録者数: 1,100万人
- 総動画数: 3,200本（直近200本を分析）
- 平均再生回数: 150万回
- 最も再生された動画: 「○○○」（5,000万回）
- いいね率平均: 4.2%
- 投稿頻度: 週3-4本（木・金が多い）