Which integrations are available for this server?

Provides a command-line interface for interacting with the MCP server endpoints, allowing users to make requests for tool listing and other server capabilities. Supports testing capabilities for the MCP server, allowing test execution with coverage reporting to validate server functionality. Enables interaction with Python-based services, supporting HDF5 file operations (read datasets, list contents), Slurm job management (submit jobs, check status), hardware monitoring, and data compression/decompression.

How do I use MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Server list contents of my simulation data HDF5 file" 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.

mcp-server

By: Zack Chaffee A20478873

A server implementing Model Coupling Protocol (MCP) capabilities for HDF5 file operations and Slurm job management.

Features

HDF5 file operations:

Read datasets
List file contents

Slurm job management:

Submit jobs
Check job status

Node Hardware Operations

Get CPU information
Get memory information
Get disk information
Get comprehensive system information

Compression Operations

Compress string data with gzip or zlib
Compress files with gzip or zlib
Decompress data

Initialization

Once you clone this reponsitory cd into it

After this hwe will create a virtual enviornment and install all dependincies:

uv venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install -e .
uv pip install -e ".[test]"

Running

To startup the server run:

python -m src.server

This will autoclocate the server at http://localhost:8000.

Related MCP server: F5 MCP Server

Endpoints

POST /mcp: Main endpoint for MCP requests
GET /health: Health check endpoint

Examples:

import httpx

async with httpx.AsyncClient() as client:
    # Read a dataset
    response = await client.post("http://localhost:8000/mcp", json={
        "capability": "hdf5",
        "action": "read_dataset",
        "parameters": {
            "file_path": "/path/to/data.h5",
            "dataset_path": "/path/to/dataset"
        }
    })
    
    # List contents
    response = await client.post("http://localhost:8000/mcp", json={
        "capability": "hdf5",
        "action": "list_contents",
        "parameters": {
            "file_path": "/path/to/data.h5",
            "group_path": "/"
        }
    })

curl -X POST http://localhost:8000/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "method": "mcp/listTools", 
    "params": {}, 
    "id": "1"
  }'