YouTube Studio MCP

Give your AI assistant your YouTube Studio data. A local Model Context Protocol server for creators: your agent (Claude, ChatGPT desktop, Cursor, VS Code, …) can read your channel analytics, dig into your audience and content, build media-kit data, and render ready-to-use charts, straight from the official YouTube APIs.

"How did my channel do this month compared to last month, and why?" "Which Shorts brought me the most subscribers this quarter?" "What are people searching for when they find my videos?" "Where do viewers drop off in my latest video?" "Update my media kit with the last 28 days and make the charts."

It runs on your computer and signs in with your own Google OAuth client. Your data goes from Google to your machine to your AI app, and nowhere else. It's read-only unless you opt in.

What it can do

Plus four prompt templates (channel_checkup, media_kit_refresh, video_deep_dive, comment_digest). The full parameter reference is in docs/tools.md.

Related MCP server: youtube-organic-mcp

Quick start

You need uv (a Python installer; brew install uv on macOS, or see the link) and about 10 minutes for the one-time Google setup.

1. Install and sign in

# Install the command (from GitHub; see "PyPI" below)
uv tool install git+https://github.com/nelsonrogers/youtube-studio-mcp

# Create a free Google OAuth client first: docs/google-cloud-setup.md (one time, ~10 min)
# Then sign in with the account that owns the channel:
youtube-studio-mcp auth --client-secret ~/Downloads/client_secret_XXXX.json

Follow docs/google-cloud-setup.md for the OAuth client. Importantly, it shows how to publish the app so you don't have to sign in again every 7 days.

auth also switches on YouTube's impressions reports, so impressions & CTR are available ~2 days later.

2. Connect your AI app

Find the full path of the command with which youtube-studio-mcp (macOS/Linux) or where youtube-studio-mcp (Windows). It's usually ~/.local/bin/youtube-studio-mcp. Desktop apps often don't see your shell's PATH, so use the full path.

Settings → Developer → Edit Config, then add:

{
  "mcpServers": {
    "youtube-studio": {
      "command": "/Users/YOU/.local/bin/youtube-studio-mcp"
    }
  }
}

Restart Claude Desktop. The tools appear under the 🔧 menu.

claude mcp add youtube-studio -- youtube-studio-mcp

In the current ChatGPT desktop app (macOS/Windows): Settings → MCP servers → Add server, choose STDIO, name it youtube-studio, set the command to the full path above, save and Restart.

The app stores this in ~/.codex/config.toml (shared with the Codex CLI), so you can also add:

[mcp_servers.youtube-studio]
command = "/Users/YOU/.local/bin/youtube-studio-mcp"

Local servers run on the desktop app's agent (Codex) side. ChatGPT on the web and older versions of the desktop app only reach remote servers. If the tools don't show up in your chat, try a Codex or agent task, or fall back to youtube-studio-mcp media-kit --out media-kit-data.json and attach the file.

Use the same mcpServers entry as Claude Desktop (e.g. in ~/.cursor/mcp.json).

.vscode/mcp.json:

{ "servers": { "youtube-studio": { "type": "stdio", "command": "youtube-studio-mcp" } } }

{
  "mcpServers": {
    "youtube-studio": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/nelsonrogers/youtube-studio-mcp", "youtube-studio-mcp"]
    }
  }
}

Append @vX.Y.Z to the git URL to pin a release.

3. Check it works

youtube-studio-mcp check     # calls every read-only tool against your channel

Then ask your assistant something like "Give me a check-up of my channel for the last 28 days."

Media kits

Media kits (sponsor decks) need the same numbers every month. get_media_kit_data returns them in one stable JSON snapshot: channel stats, period performance vs the previous period, Shorts vs long-form, the impressions funnel, daily and monthly trends, audience (age, gender, countries, devices), recent uploads with thumbnails, top videos and traffic sources. Each part degrades gracefully when YouTube can't provide it.

• Field reference and stability promise: docs/media-kit-data.md

• Sample output (fictional channel): docs/examples/media-kit-data.sample.json

• No AI client needed: youtube-studio-mcp media-kit --out media-kit-data.json

• Charts: ask the agent to run create_chart on the snapshot, or use the media_kit_refresh prompt.

Configuration

All settings are environment variables, so you can set them in your MCP client's env block.

CLI

youtube-studio-mcp [serve]          run the MCP server over stdio (what MCP clients launch)
youtube-studio-mcp auth             sign in (--client-secret PATH, --write, --no-revenue, --no-browser)
youtube-studio-mcp status           show sign-in state and configuration
youtube-studio-mcp check            call every read-only tool against your channel
youtube-studio-mcp media-kit        write the media-kit JSON (--out, --period, --start-date, --end-date)
youtube-studio-mcp logout           revoke access and delete the local token

Multiple channels

youtube-studio-mcp auth --profile gaming

Then add a second server entry with "env": {"YOUTUBE_STUDIO_MCP_PROFILE": "gaming"}.

Editing tools (opt-in)

Off by default. To let your agent edit titles/descriptions/tags/visibility, reply to comments and moderate them:

youtube-studio-mcp auth --write

…and set "env": {"YOUTUBE_STUDIO_MCP_ENABLE_WRITE": "1"}. update_video_details defaults to a dry run that shows a before/after diff. Agents are told to confirm with you before saving or posting.

Good to know

• Freshness: YouTube Analytics data lags 48-72 hours, and dates are US Pacific time. Rolling periods end at the latest complete day, and every result states its exact date range. There is no API for Studio's realtime/48-hour card.

• Subscriber counts: the public count is rounded by YouTube (48,409 → 48,400), even for the owner. Analytics-based totals (totalSubscribers, subscribers_from_analytics) are exact but a few days old.

• Demographics are shares of logged-in viewers and are withheld for low-traffic periods.

• Shorts vs long-form: analytics tools use YouTube's own creatorContentType classification; list_videos can only guess from duration (Shorts can be up to 3 minutes).

• Impressions & CTR aren't in the Analytics API. They come from the YouTube Reporting API (daily files, ~2-day delay, history starts 30 days before reports were enabled).

• Quota: listing uses ~1 unit per 50 videos out of a default 10,000/day; no search.list calls.

• Owner only: the APIs return analytics only for channels the signed-in account owns (Studio "manager" access isn't enough).

Privacy & security

• Runs locally over stdio. There's no server, telemetry or third-party service.

• Tokens are stored in your config folder with owner-only permissions (0600).

• Read-only Google scopes by default; write access is a separate opt-in.

• Sign-in uses the OAuth loopback flow with PKCE and state checks. Nothing is printed to the JSON-RPC stream.

• See SECURITY.md to report a vulnerability.

Development

git clone https://github.com/nelsonrogers/youtube-studio-mcp && cd youtube-studio-mcp
uv sync
uv run pytest                         # tests run against a rule-checking fake YouTube API
uv run ruff check . && uv run ruff format --check . && uv run mypy
npx @modelcontextprotocol/inspector uv run youtube-studio-mcp   # optional: try tools in the MCP Inspector

Architecture in one minute:

cli.py ──► server.py (MCPServer + instructions) ──► tools/*.py  (one module per area)
                                                    │
               context.py (shared state, arg types, error → ToolError, output paths)
               analytics.py (report runner, daily → week/month aggregation, labels)
               youtube.py (thread-safe Google API client, error translation, Data API helpers)
               auth.py (OAuth loopback flow, token store)   charts.py (matplotlib renderer)

See CONTRIBUTING.md for guidelines.

PyPI

The name youtube-studio-mcp is already taken on PyPI by an unrelated project, so this package is installed from GitHub for now. Don't run pip install youtube-studio-mcp / uvx youtube-studio-mcp expecting this project.

Roadmap

• Publish to PyPI and the MCP Registry under a free name

• Optional Streamable-HTTP transport for remote setups (ChatGPT web, hosted agents)

• Local history snapshots for trends beyond the API's retention

• Playlist analytics and live-stream concurrency reports as dedicated tools

License

MIT. Not affiliated with, endorsed or sponsored by YouTube or Google. "YouTube" is a trademark of Google LLC.