Give your AI assistant a video studio over MCP (and the tool pattern that keeps it from burning your credits)

Most MCP demos read files or query databases. Those tools are cheap and reversible. The interesting design problem starts when a tool spends money: generating a video can cost real credits, and an assistant that calls it in a loop can empty a balance before you notice.

This post does two things. First, a five-minute setup that gives Claude Code or Cursor video, image and voice generation over a remote MCP server. Second, the tool design that makes it safe to hand an assistant a paid tool. The pattern works for any MCP server that costs money to run, not just this one.

Disclosure: the server in the examples is Vidofy's, which is our own product. The setup steps and the pattern are general.

A local MCP server is a process you install and run next to your client, usually with an API key in an environment variable. A remote server is just a URL. You add the URL, your client opens a browser sign-in, you approve it, and the client holds the permission. There is nothing to install and no key to paste into a config file or leave in a chat log.

That second part matters more than it sounds. A key in mcp.json ends up in dotfiles repos and screenshots. A permission you approved in the browser can be revoked per client, and the other clients keep working.

claude mcp add --transport http vidofy https://vidofy.ai/mcp-app Enter fullscreen mode Exit fullscreen mode Then, inside Claude Code, run /mcp, pick vidofy, and approve the sign-in in the browser that opens. After that, ask for an image or a video in the same session.

Add this to ~/.cursor/mcp.json, or.cursor/mcp.json in your project:

{ "mcpServers": { "vidofy": { "url": "https://vidofy.ai/mcp-app" } } } Enter fullscreen mode Exit fullscreen mode Enable it in Cursor's MCP settings and approve the sign-in. Claude.ai, Claude Desktop, ChatGPT, VS Code and Codex have the same flow with their own menus.

Some clients cannot complete a browser sign-in because they expect to register themselves. For those, the same package runs locally against a personal token:

{ "mcpServers": { "vidofy": { "command": "npx", "args": [ "-y", "@vidofy/mcp" ], "env": { "VIDOFY_TOKEN": "vmt_..." } } } } Enter fullscreen mode Exit fullscreen mode You create that token once in your account settings. Same account, same balance.

Here is what an assistant sees when you ask for "a 9:16 clip of a cyclist riding through neon-lit rain, about 5 seconds, and tell me what it costs first":

The important part is step 3. Generation is slow and costs money, so the server separates deciding from doing:

If you are building an MCP server around anything metered (GPU time, API calls, SMS, renders), copy that split: one tool to look up what is possible, one to price a specific request, one to commit. You give up one round trip and get back a spending limit the user can see.

A few requests that work well in plain sentences:

On clients that support MCP Apps, such as Claude, the result arrives as a live card that updates while the clip renders. On a text-only client the same information comes back as text.

The connector page lists every client's exact steps, the model catalogue and the nine tools: vidofy.ai/en/mcp. New accounts start with free credits, so you can run the flow above without paying anything first.

If you build MCP servers yourself, I would like to hear how you handle paid tools. Do you price first like this, or cap spend some other way?