Mcp— agent skill

CutPro MCP

A Model Context Protocol (MCP) server that turns long videos into viral clips with AI. It exposes the full CutPro API as tools, so an LLM can run the whole flow: analyze a video, clip the best moments, render the final MP4, and publish to TikTok, Instagram and YouTube.

Key features

• End to end. All 34 v1 endpoints as tools: workspace, balance, videos, clipping, clips, templates, renders, posts and connections.
• Token efficient. Results are compact and projected to the fields that matter; list_clips is rating sorted, capped, and omits long signed URLs unless asked.
• Runs everywhere. stdio for local clients (Claude Code, Cursor, Claude Desktop, Windsurf, VS Code, Cline, Zed) and a hosted Streamable HTTP endpoint with OAuth for ChatGPT and Claude.ai.

Getting started

Requirements

• Node.js 18 or newer.
• A CutPro account on the Pro plan and an API key. Generate one at cut.pro/studio/me/api-keys.
• An MCP-compatible client.

Standard config

Most clients use the same JSON. Add your API key under env:

{
  "mcpServers": {
    "cutpro": {
      "command": "npx",
      "args": ["-y", "@cutpro/mcp"],
      "env": { "CUTPRO_API_KEY": "<your-api-key>" }
    }
  }
}

Install

<img src="https://img.shields.io/badge/VS_Code-Install_Server-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white" alt="Install in VS Code"> <img src="https://img.shields.io/badge/Cursor-Install_Server-000000?style=flat-square&logo=cursor&logoColor=white" alt="Install in Cursor"> <img src="https://img.shields.io/badge/LM_Studio-Install_Server-4A26C9?style=flat-square" alt="Install in LM Studio"> <img src="https://img.shields.io/badge/Goose-Install_Server-1A1A1A?style=flat-square" alt="Install in Goose">

After installing via a button, add your CUTPRO_API_KEY to the server's env.

<details> <summary>Claude Code</summary>

claude mcp add cutpro --env CUTPRO_API_KEY=<your-api-key> -- npx -y @cutpro/mcp

</details>

<details> <summary>Claude Desktop</summary>

Add to claude_desktop_config.json (Settings, Developer, Edit Config):

{
  "mcpServers": {
    "cutpro": {
      "command": "npx",
      "args": ["-y", "@cutpro/mcp"],
      "env": { "CUTPRO_API_KEY": "<your-api-key>" }
    }
  }
}

</details>

<details> <summary>Cursor / Windsurf / VS Code (manual)</summary>

Add the standard config above to the client's MCP settings (mcp.json / mcpServers). </details>

<details> <summary>Cline</summary>

Open the MCP Servers panel, choose Configure, and add the standard config above. </details>

<details> <summary>Gemini CLI</summary>

gemini mcp add cutpro npx -y @cutpro/mcp -e CUTPRO_API_KEY=<your-api-key>

</details>

<details> <summary>Codex</summary>

Add to ~/.codex/config.toml:

[mcp_servers.cutpro]
command = "npx"
args = ["-y", "@cutpro/mcp"]
env = { "CUTPRO_API_KEY" = "<your-api-key>" }

</details>

<details> <summary>ChatGPT and Claude.ai (hosted, no install)</summary>

Use the hosted server. Add a custom connector pointing to:

https://mcp.cut.pro

You authorize with your CutPro API key on a consent page (OAuth), so no local setup is needed. </details>

Configuration

The server is configured with environment variables.

<details> <summary>Self-hosting the remote (Streamable HTTP + OAuth)</summary>

MCP_TRANSPORT=http PORT=8787 MCP_OAUTH=1 \
MCP_PUBLIC_URL=https://mcp.cut.pro MCP_REDIS_URL=redis://127.0.0.1:6379 \
npx -y @cutpro/mcp

In OAuth mode the user authorizes with their own API key on a consent page; the access token maps server side to that key. Without MCP_REDIS_URL, an in-memory store is used (single instance, state lost on restart). </details>

Tools

<details> <summary>Workspace and balance</summary>

• get_workspace: the workspace this key resolved to, with plan and role.
• get_balance: current credit balance.
• get_balance_history: ledger of credits added and consumed.

</details>

<details> <summary>Videos and uploads</summary>

• list_videos: your source video library.
• start_upload: get a presigned URL to upload your own file (max 2 GB; .mp4/.mov/.webm/.mkv).
• complete_upload: register a finished upload and get its credit cost.
• delete_video: delete a source video and its submissions.

</details>

<details> <summary>Clipping</summary>

• analyze_video: preview metadata and credit cost of a public URL (free).
• submit_clipping: start AI clipping (charges credits).
• list_submissions: clipping jobs for a video.
• get_submission: poll a submission until completed or failed.
• delete_submission: delete a submission and its clips.

</details>

<details> <summary>Clips and templates</summary>

• list_clips: clips of a completed submission, rating sorted (URLs opt-in).
• apply_template: apply an editing template to clips in bulk.
• delete_clip: delete a single clip.
• list_templates: editing templates to apply to clips.

</details>

<details> <summary>Renders</summary>

• render_clip: render a clip to a final MP4.
• get_render: poll a render until completed.
• get_render_download: signed download URL of a completed render.
• cancel_render: cancel or delete a render.
• get_render_limits: render quota for the workspace.
• start_bulk_download / get_bulk_download: bundle several renders into one download.

</details>

<details> <summary>Posts and connections</summary>

• create_post: publish rendered clips to connected accounts (immediate or scheduled).
• list_posts / get_post / update_post / delete_post: manage posts.
• publish_post: trigger publishing now.
• retry_post_item / delete_post_item: handle individual targets.
• list_connections / get_connection: connected social accounts.

</details>

Each tool carries read-only / write / destructive annotations so clients can plan calls.

Links

• Docs: cut.pro/docs/api-reference/mcp
• npm: @cutpro/mcp
• MCP Registry: io.github.getcutpro/cutpro

License

MIT