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MCP Server Coreshub
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MCP Server Coreshub

Coreshub MCP Server is an MCP server implementation based on Python, providing pluginized tools and prompt functions, and supporting running in the Cherry Studio or command line environment. The project includes modules such as tool management, configuration settings, and server implementation, and developers can easily add custom tools and prompts.
Cloud platformDeveloper tools#Pluginized tools#MCP service#Python implementation#Coreshub Computing.Python
2 points
9.0K
Open Site

Overview

Installation

Content Details

Alternatives

What is Coreshub MCP Server?

Coreshub MCP Server is a middleware service that connects AI models with various resources of the Coreshub Computing Platform through a standardized protocol, including file systems (EPFS), container instances, distributed training tasks, and inference services.

How to use Coreshub MCP Server?

It can be used in two ways: through the Cherry Studio visual interface or the command line. The service provides a variety of tool plugins to query and manage platform resources.

Applicable scenarios

It is suitable for scenarios where you need to query or manage the resources of the Coreshub Computing Platform during the interaction with AI models, such as obtaining container SSH information, viewing training task logs, and querying file system bills.

Main features

Resource query
Provide a variety of tool plugins to query the status of platform resources, including container instances, file systems, training tasks, and inference services
Log acquisition
Support obtaining detailed log information of distributed training tasks and inference services
SSH management
Query the SSH connection information of container instances for convenient remote access
Bill query
Provide the function of querying the bill information of the EPFS file system
Plugin extension
Support developers to customize tools and prompt plugins to extend the server functions
Advantages
A unified resource management interface simplifies the interaction between AI models and platform resources
A flexible plugin mechanism supports function extension
Support multiple usage methods, including visual interface and command line
A detailed log query function facilitates problem troubleshooting
Limitations
You need a Coreshub Computing Platform account to use it
Some functions require model services with more than 32B parameters
The command line method requires a certain technical background

How to use

Configure environment variables
Set the access key and account ID of Coreshub Computing
Start the service
Configure in Cherry Studio or start the service through the command line
Use tool plugins
Call various tool plugins through AI models to query or manage resources

Usage examples

Query running containers
When you need to know the status of currently running container instances
Get training task logs
When you need to view the running logs of distributed training tasks
Query file system usage
When you need to know the storage usage and costs of the EPFS file system

Frequently asked questions

Why do I need to configure environment variables?
How do I know which tool plugins are available?
What should I do if the service fails to start?
Can I customize tool plugins?

Related resources

GitHub repository
Project source code
uv documentation
uv tool usage documentation
Coreshub Computing Platform
Official website of Coreshub Computing

Installation

Copy the following command to your Client for configuration
{
  "mcpServers": {
    "coreshub-mcp-server": {
      "type": "stdio",
      "registryUrl": "http://mirrors.aliyun.com/pypi/simple/",
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/coreshub/mcp-server-coreshub",
        "coreshub-mcp-server"
      ],
      "env": {
        "QY_ACCESS_KEY_ID": "基石智算的AK",
        "QY_SECRET_ACCESS_KEY": "基石智算的SK",
        "CORESHUB_USER_ID": "基石智算的账户ID"
      }
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.

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