An MCP server that integrates with [Scenext](https://scenext.cn) AI video generation platform to create educational explanation videos based on
If you want an AI assistant working directly with Scenext MCP, the scenext mcp mcp server is the bridge. An MCP server that integrates with Scenext AI video generation platform to create educational explanation videos based on topics.
An MCP server that integrates with Scenext AI video generation platform to create educational explanation videos based on topics.
Setup follows the standard MCP pattern: clone or install the server, then register it in your client's configuration file and restart the client. The configuration snippets on this page cover Claude Desktop, Claude Code and Cursor.
Before the server will start you need to supply one environment variable: SCENEXT_API_KEY. Keep credentials in your client's env block or a secrets manager rather than committing them.
Developer-tool servers are usually the first ones people connect, because they turn "help me with this code" into an assistant that can actually read the repo and act on it. Scenext MCP sits in that group. Worth comparing against the other developer tools servers in this directory before you commit to one, since several overlap in scope but differ sharply in setup cost and permissions.
{
"mcpServers": {
"scenext": {
"command": "uvx",
"args": ["scenext-mcp"],
"env": {
"SCENEXT_API_KEY": "your_actual_api_key_here"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| SCENEXT_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.