Novita MCP Server
Official# Novita MCP Server
[](https://smithery.ai/server/@novitalabs/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](https://claude.ai/download), [Cursor](https://www.cursor.com/), or any other compatible MCP client.
<a href="https://glama.ai/mcp/servers/@novitalabs/novita-mcp-server">
<img width="380" height="200" src="https://glama.ai/mcp/servers/@novitalabs/novita-mcp-server/badge" alt="Novita Server 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](https://novita.ai/gpus-console).
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**
```bash
npm install -g @novitalabs/novita-mcp-server
```
**Using Smithery**
Visit the [https://smithery.ai/server/@novitalabs/novita-mcp-server](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](https://novita.ai/settings/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](https://modelcontextprotocol.io/quickstart/user) guide to learn how to configure the MCP server.
>
> For Cursor, you can refer to the [Cursor MCP Quickstart](https://docs.cursor.com/context/model-context-protocol) guide to learn how to configure the MCP server.
```json
{
"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
```txt
List all the Novita clusters
```
### List products
```txt
List all available Novita GPU instance products
```
### List GPU instances
```txt
List all my running Novita GPU instances
```
### Create a new GPU instance
```txt
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:
```bash
npm test
```TDQS
Scored across 20 tools
Each tool has a clearly distinct purpose targeting specific resources and actions. The naming convention makes it easy to differentiate between operations on different resource types like GPU instances, templates, network storage, and container registry auths. There is no ambiguity about which tool to use for a given task.
Tool names follow a perfectly consistent verb_noun pattern throughout, using snake_case uniformly. The pattern is clear: action_resource (e.g., create_gpu_instance, list_templates, delete_network_storage). This consistency makes the tool set highly predictable and easy to navigate.
With 20 tools, the count is slightly high but reasonable for a cloud infrastructure server covering multiple resource types. It provides comprehensive operations for GPU instances, templates, network storage, and container registry auths, though it might feel a bit heavy compared to more focused servers.
The tool set offers good CRUD/lifecycle coverage for its domain, with create, delete, list, and specific operations like start/stop/restart for GPU instances. Minor gaps exist, such as missing update operations for GPU instances and templates, but agents can work around these with the available tools.