@vidalytics/mcp

MCP serverDatabases & data

Once this app is added, your AI can manage your Vidalytics videos and analytics for you. You can handle video and reporting tasks by asking your assistant in plain language instead of working in Vidalytics directly.

Available today. Use it from your connected AI after setup.

Add the app, then try asking your AI to show your Vidalytics videos or pull up your video analytics.

Then ask your AI: use @vidalytics/mcp

What your AI can do with it

  • Manage the videos in your Vidalytics account
  • Look up analytics for your videos
  • Answer questions about your video performance data
  • Handle Vidalytics tasks without leaving your conversation with your AI

From the project's README

As published by vidalytics/vidalytics-mcp in README.md.

One-command setup that connects your AI coding assistant to Vidalytics video analytics data via the Model Context Protocol.

Works with Claude (CLI & Desktop), Windsurf, Cursor, and any other MCP-compatible client.

Setup

npx @vidalytics/mcp install

That's it. The installer detects which AI clients you have installed, lets you pick which ones to configure, and wires them up. Restart the client — a browser window will open for OAuth authorization on first use.

Cursor

Or via the installer:

npx @vidalytics/mcp install --client cursor

Or manually — add this to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "vidalytics": {
      "url": "https://api.vidalytics.com/public/v1/mcp"
    }
  }
}

Restart Cursor. On first use a browser window opens for OAuth authorization with your Vidalytics account — no API key or environment variables to set.

What it does

  • Detects installed MCP clients (Claude CLI, Claude Desktop, Windsurf, Cursor) by checking config files, app directories, binaries in $PATH, and app bundles (e.g. /Applications on macOS)
  • Presents an interactive checklist (detected clients pre-selected) so you configure exactly the ones you want — or pick them non-interactively with --client
  • Adds Vidalytics as an MCP server in each selected client's config
  • Verifies after writing: the config is valid and the MCP server is reachable
  • Non-interactive terminals (CI) and any explicit selection flag (--client, --all, --yes) skip the checklist and behave predictably

Available tools

Once connected, your AI assistant gains access to:

ToolDescription
set_user_contextMUST be called before any other tool to enable analytics
list_videosList videos with pagination
get_videoGet video details
get_video_by_embed_guidFind a video by its embed GUID
update_videoUpdate a video's title or folder
get_video_embedGet the embed code and configuration
get_video_settingsGet playback settings (autoplay, controls, etc)
get_video_thumbnailGet the thumbnail image URL
get_video_statsViews, play rate, watch time, conversions
get_video_dropoffAudience retention by percentage
get_video_percentage_watched% of viewers who reached each point
get_video_live_metricsReal-time active viewers and watch rate
get_video_ctasGet CTAs for a video
get_video_pause_screensGet pause screens for a video
get_videos_stats_batchStats for up to 30 videos at once
get_videos_timelineTimeline stats for up to 5 videos
list_foldersList video folders
list_settings_templatesList settings templates
get_api_usageGet current API usage and quota
list_connectionsList apps connected to your account
revoke_connectionDisconnect an app or yourself
upload_video_from_urlUpload a video from a remote URL
get_video_upload_urlGet a signed URL for local file upload
validate_uploadComplete a direct video upload
publish_videoPublish a video's pending draft settings
duplicate_videoDuplicate a video and publish the copy
create_folderCreate a video folder, optionally nested under another folder
apply_settings_templateApply a reusable player settings template to a video
create_video_ctaCreate a call-to-action on a video
update_video_ctaUpdate an existing call-to-action on a video
set_video_thumbnail_from_urlSet a video's thumbnail from a public image URL
set_video_thumbnail_from_frameSet a video's thumbnail from one of its frames
delete_video_thumbnailRemove a custom thumbnail and restore the default

Options

npx @vidalytics/mcp install [flags]

  --client <names>   Configure only these clients, comma-separated
                     (claude-cli, claude-desktop, windsurf, cursor)
  --all              Configure all known clients, even if not detected
  --config <path>    Also configure a custom config file (repeatable)
  --force            Re-apply even if already configured
  --yes              Skip prompts (configure detected clients)

Run with no flags in an interactive terminal to get a checklist of clients to configure (detected ones are pre-selected; use space to toggle, enter to confirm). --client cursor,windsurf does the same selection non-interactively.

The --config flag can be repeated for multiple files. The target file must follow the { "mcpServers": {} } format used by Claude Desktop, Cursor, and Windsurf — useful for unsupported clients like Zed or VS Code with an MCP plugin.

Troubleshooting

Authorization issues, or need to re-authenticate? Reset the credentials that mcp-remote caches in your home directory, then restart the client:

OSCommand
macOS / Linuxrm -rf ~/.mcp-auth
Windows (CMD)rd /s /q "%USERPROFILE%\.mcp-auth"
Windows (PowerShell)Remove-Item -Recurse -Force "$HOME\.mcp-auth"

MCP Registry

This server is published to the official MCP Registry as com.vidalytics/mcp. It is a remote (streamable-http) server, so registry-aware MCP clients can connect to it directly at:

https://api.vidalytics.com/public/v1/mcp

No API key or environment variables are required — authorization is handled via OAuth on first use.

Requirements

  • Node.js 18+
  • A Vidalytics account

License

MIT

Signals

Last commit
Aug 2026
Advanced
Delivery
mcp MCP server → your ahel gateway (mcp.ahel.ai) → every connected AI client.
Catalog kind
mcp-server
Gateway key
com-vidalytics-mcp
Source
github.com/vidalytics/vidalytics-mcp
Hosted endpoint
https://api.vidalytics.com/public/v1/mcp