Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@iLoveVideoEditor MCP Server render a video from template 'intro' with color=blue"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Model Context Protocol (MCP) server that lets AI agents (Claude, ChatGPT, Cursor, and any MCP client) render videos with iLoveVideoEditor: list motion-design templates, fill in variables, submit JSON-to-MP4 render jobs, poll status, and get download URLs.
Get an API key from the dashboard: ilovevideoeditor.com/dashboard/account/api
Add the server to your MCP client config:
{
"mcpServers": {
"ilovevideoeditor": {
"command": "npx",
"args": ["-y", "@ilovevideoeditor/mcp-server"],
"env": {
"VF_API_KEY": "vf_live_xxx"
}
}
}
}Related MCP server: atsurae
Tool | Description |
| List available templates |
| Get template details and variable schema |
| Submit raw |
| Render from a named template + variables |
| Poll render job status |
| Get signed download URL |
Variable | Default | Description |
| — | Required. Your iLoveVideoEditor API key ( |
|
| API base URL override |
npm install
npm run build # tsc -> dist/
npm start # stdio transport
npm run dev # tsx watchThe server speaks MCP over stdio (diagnostics go to stderr). An optional
--http flag enables HTTP/SSE transport for debugging.
Source of truth lives in the iLoveVideoEditor monorepo (integrations/mcp-server);
this repository is the public mirror used for releases.
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We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/ilovevideoeditor/mcp-server'
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