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.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Serverlist 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.serverThis will autoclocate the server at http://localhost:8000.
Related MCP server: F5 MCP Server
Endpoints
POST /mcp: Main endpoint for MCP requestsGET /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"
}'Testing
For testing rung:
pytestFor tests with coverage:
pytest --cov=srcThis server cannot be installed
Resources
Looking for Admin?
Admins can modify the Dockerfile, update the server description, and track usage metrics. If you are the server author, to access the admin panel.