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

A developer at Vidofy published a walkthrough for connecting Claude Code and Cursor to a remote MCP server that generates video, image and voice content, using browser-based OAuth sign-in instead of API keys in config files. The post's core argument is a tool-design pattern for paid MCP tools: split capability lookup (list_models, get_model) from cost estimation (estimate_cost) and execution (generate, get_status, get_result), so an assistant can price a request before committing credits. The author recommends the split for any metered MCP server, such as GPU time, SMS or renders, trading one extra round trip for a visible spending limit.

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 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" } } } 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 ..." } } } } 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": list models : looks for a model that does 9:16 at 5 seconds. get model : reads that model's input contract which fields exist, which are required, which durations are valid . estimate cost : returns the credit cost generate , then get status , then get result : run it, follow it while it renders, return the file. 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 https://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?