Novita MCP Server
About
The Model Context Protocol (MCP) server that provides seamless interaction with Novita AI platform resources
Details
- Author
- novitalabs
- GitHub stars
- 12
- Downloads
- 337
- Categories
- Cloud Service, AI, API, Other
Jump to
- List, get, create, start, stop, delete, and restart GPU instances
- List clusters/regions and GPU instance products
- Manage templates (list, get, create, delete)
- Manage container registry authentication (list, create, delete)
- Manage network storage (list, create, update, delete)
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Novita MCP ServerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Install the package globally via npm (npm install -g @novitalabs/novita-mcp-server) or using Smithery. Obtain a Novita API key from the Novita AI Key Management page. Then configure the server in your MCP client (Claude Desktop or Cursor) by adding a JSON entry with the command npx -y @novitalabs/novita-mcp-server and the environment variable NOVITA_API_KEY.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"novita mcp server": {
"@novitalabs/novita-mcp-server": {
"command": "npx",
"args": [
"-y",
"@novitalabs/novita-mcp-server"
],
"env": {
"NOVITA_API_KEY": ""
}
}
}
}
}McpServers
{
"@novitalabs/novita-mcp-server": {
"command": "npx",
"args": [
"-y",
"@novitalabs/novita-mcp-server"
],
"env": {
"NOVITA_API_KEY": ""
}
}
}Novita MCP Server
novita-mcp-server is a Model Context Protocol (MCP) server that provides seamless interaction with Novita AI platform resources. We recommend accessing this server through Claude Desktop, Cursor, or any other compatible MCP client.
<a href="https://glama.ai/mcp/servers/@novitalabs/novita-mcp-server">
</a>
Features
> ⚠️ Beta Notice: novita-mcp-server is currently in beta and only supports GPU instance management. Additional resource types will be supported in future releases.
Currently, novita-mcp-server enables management the resources of GPU instances product.
Supported operations are as follows:
- Cluster(/Region): List;
- Product: List;
- GPU Instance: List, Get, Create, Start, Stop, Delete, Restart;
- Template: List, Get, Create, Delete;
- Container Registry Auth: List, Create, Delete;
- Network Storage: List, Create, Update, Delete;
Installation
You can install the package using npm, or Smithery:
Using npm
npm install -g @novitalabs/novita-mcp-server
Using Smithery
Visit the https://smithery.ai/server/@novitalabs/novita-mcp-server and follow the "Install" instructions to install the server.
Configuration to use novita-mcp-server
First, you need to get your Novita API key from the Novita AI Key Management.
And next, you can use the following configuration for both Claude Desktop and Cursor:
> 📌 Tips
>
> For Claude Desktop, you can refer to the Claude Desktop MCP Quickstart guide to learn how to configure the MCP server.
>
> For Cursor, you can refer to the Cursor MCP Quickstart guide to learn how to configure the MCP server.
{
"mcpServers": {
"@novitalabs/novita-mcp-server": {
"command": "npx",
"args": ["-y", "@novitalabs/novita-mcp-server"],
"env": {
"NOVITA_API_KEY": "your_api_key_here"
}
}
}
}
Examples
Here are some examples of how to use the novita-mcp-server to manage your resources with Claude Desktop or Cursor:
List clusters
List all the Novita clusters
List products
List all available Novita GPU instance products
List GPU instances
List all my running Novita GPU instances
Create a new GPU instance
Create a new Novita GPU instance:
Name: test-novita-mcp-server-01
Product: any available product
GPU Number: 1
Image: A standard public PyTorch/CUDA image
Container Disk: 60GB
Testing
This project uses Jest for testing. The tests are located in the src/__tests__ directory.
You can run the tests using one of the following commands:
npm test
Sign in to leave a review
Use Google, GitHub, or an email account so ratings stay tied to real people.
No reviews posted yet.
