
CLIO Node Hardware
- 25 repo stars
- Updated July 24, 2026
- iowarp/clio-kit
CLIO Node Hardware is an MCP server that supplies AI agents with node hardware monitoring and system analysis on the local machine.
About
CLIO Node Hardware (io.github.iowarp/node-hardware-mcp) provides a Model Context Protocol interface for comprehensive hardware monitoring and system analysis on the machine where the server runs. developers operating their own inference boxes, build servers, or small HPC nodes can register it so agents diagnose throttling, memory pressure, or misconfigured workloads using live node facts instead of guesses. Distribution follows the clio-kit PyPI package at version 2.2.3 with stdio transport and source in iowarp/clio-kit. It shines during monitoring and perf iteration when you need quick, conversational summaries of hardware state alongside logs and metrics you already collect. It is not a full hosted observability platform replacement; it is a local MCP bridge for node introspection. Use least-privilege hosts because hardware tools can surface sensitive system detail.
- Node-level hardware monitoring and system analysis exposed to LLMs
- CLIO Node Hardware MCP in clio-kit 2.2.3 with stdio transport
- Suited to capacity checks before batch jobs or inference workloads
- Shares clio-kit packaging with other IOWarp scientific MCP servers
- Helps agents ground recommendations in actual CPU, memory, or device context
CLIO Node Hardware by the numbers
- Data as of Jul 25, 2026 (Skillselion catalog sync)
claude mcp add clio-kit -- uvx clio-kitAdd your badge
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| repo stars | ★ 25 |
|---|---|
| Package | clio-kit |
| Transport | STDIO |
| Auth | None |
| Last updated | July 24, 2026 |
| Repository | iowarp/clio-kit ↗ |
What it does
Give your agent read-oriented hardware and system analysis on a node so you can debug performance and capacity without manual sysfs spelunking.
Who is it for?
Indies self-hosting APIs, fine-tuning workstations, or edge nodes who want MCP-grounded hardware context in Claude Code or Cursor.
Skip if: Skip if you only use fully managed serverless with no SSH or node access, or those needing enterprise APM dashboards exclusively.
What you get
Once connected, your agent can call node hardware tools to align tuning, job sizing, and incident hypotheses with measured system state.
- Live node hardware and system analysis tools for your agent
- Better-grounded capacity and performance recommendations during ops
- CLIO Node Hardware MCP via clio-kit 2.2.3
By the numbers
- Version 2.2.3
- Transport type: stdio
- PyPI identifier: clio-kit
README.md
CLIO Kit
CLIO Kit - Part of the IoWarp platform's tooling layer for AI agents. A comprehensive collection of tools, skills, plugins, and extensions. Currently featuring 15+ Model Context Protocol (MCP) servers for scientific computing, with plans to expand to additional agent capabilities. Enables AI agents to interact with HPC resources, scientific data formats, and research datasets.
Chat with us on Zulip or join us
Developed by
Gnosis Research Center
❌ Without CLIO Kit
Working with scientific data and HPC resources requires manual scripting and tool-specific knowledge:
- ❌ Write custom scripts for every HDF5/Parquet file exploration
- ❌ Manually craft Slurm job submission scripts
- ❌ Switch between multiple tools for data analysis
- ❌ No AI assistance for scientific workflows
- ❌ Repetitive coding for common research tasks
✅ With CLIO Kit
AI agents handle scientific computing tasks through natural language:
- ✅ "Analyze the temperature dataset in this HDF5 file" - HDF5 MCP does it
- ✅ "Submit this simulation to Slurm with 32 cores" - Slurm MCP handles it
- ✅ "Find papers on neural networks from ArXiv" - ArXiv MCP searches
- ✅ "Plot the results from this CSV file" - Plot MCP visualizes
- ✅ "Optimize memory usage for this pandas DataFrame" - Pandas MCP optimizes
- ✅ "Find all documents where pressure exceeds 200 kPa" - Agentic Search retrieves
One unified interface. 16 MCP servers. Hybrid search engine. 150+ specialized tools. Built for research.
CLIO Kit is part of the IoWarp platform's comprehensive tooling ecosystem for AI agents. It brings AI assistance to your scientific computing workflow—whether you're analyzing terabytes of HDF5 data, managing Slurm jobs across clusters, or exploring research papers. Built by researchers, for researchers, at Illinois Institute of Technology with NSF support.
Part of IoWarp Platform: CLIO Kit is the tooling layer of the IoWarp platform, providing skills, plugins, and extensions for AI agents working in scientific computing environments.
One simple command. Production-ready, fully typed, MIT licensed, and beta-tested in real HPC environments.
🚀 Quick Installation
One Command for Any Server
# List all 16 available MCP servers
uvx clio-kit mcp-servers
# Run any server instantly
uvx clio-kit mcp-server hdf5
uvx clio-kit mcp-server pandas
uvx clio-kit mcp-server slurm
# Agentic search — hybrid retrieval for scientific corpora
uvx clio-kit search serve # Start search API server
uvx clio-kit search query --namespace local_fs --q "pressure > 200 kPa"
# AI prompts also available
uvx clio-kit prompts # List all prompts
uvx clio-kit prompt code-coverage-prompt # Use a prompt
Install in Cursor
Add to your Cursor ~/.cursor/mcp.json:
{
"mcpServers": {
"hdf5-mcp": {
"command": "uvx",
"args": ["clio-kit", "mcp-server", "hdf5"]
},
"pandas-mcp": {
"command": "uvx",
"args": ["clio-kit", "mcp-server", "pandas"]
},
"slurm-mcp": {
"command": "uvx",
"args": ["clio-kit", "mcp-server", "slurm"]
}
}
}
See Cursor MCP docs for more info.
Install in Claude Code
# Add HDF5 MCP
claude mcp add hdf5-mcp -- uvx clio-kit mcp-server hdf5
# Add Pandas MCP
claude mcp add pandas-mcp -- uvx clio-kit mcp-server pandas
# Add Slurm MCP
claude mcp add slurm-mcp -- uvx clio-kit mcp-server slurm
See Claude Code MCP docs for more info.
Install in VS Code
Add to your VS Code MCP config:
"mcp": {
"servers": {
"hdf5-mcp": {
"type": "stdio",
"command": "uvx",
"args": ["clio-kit", "mcp-server", "hdf5"]
},
"pandas-mcp": {
"type": "stdio",
"command": "uvx",
"args": ["clio-kit", "mcp-server", "pandas"]
}
}
}
See VS Code MCP docs for more info.
Install in Claude Desktop
Edit claude_desktop_config.json:
{
"mcpServers": {
"hdf5-mcp": {
"command": "uvx",
"args": ["clio-kit", "mcp-server", "hdf5"]
},
"arxiv-mcp": {
"command": "uvx",
"args": ["clio-kit", "mcp-server", "arxiv"]
}
}
}
See Claude Desktop MCP docs for more info.
Available Packages
| 📦 Package | 📌 Ver | 🔧 System | 📋 Description | ⚡ Install Command |
|---|---|---|---|---|
adios |
2.0.1 | Data I/O | Read data using ADIOS2 engine | uvx clio-kit mcp-server adios |
arxiv |
2.0.1 | Research | Fetch research papers from ArXiv | uvx clio-kit mcp-server arxiv |
chronolog |
2.0.1 | Logging | Log and retrieve data from ChronoLog | uvx clio-kit mcp-server chronolog |
compression |
2.0.1 | Utilities | File compression with gzip | uvx clio-kit mcp-server compression |
darshan |
2.0.1 | Performance | I/O performance trace analysis | uvx clio-kit mcp-server darshan |
hdf5 |
2.0.1 | Data I/O | HPC-optimized scientific data with 27 tools, AI insights, caching, streaming | uvx clio-kit mcp-server hdf5 |
jarvis |
2.0.1 | Workflow | Data pipeline lifecycle management | uvx clio-kit mcp-server jarvis |
lmod |
2.0.1 | Environment | Environment module management | uvx clio-kit mcp-server lmod |
ndp |
2.0.1 | Data Protocol | Search and discover datasets across CKAN instances | uvx clio-kit mcp-server ndp |
node-hardware |
2.0.1 | System | System hardware information | uvx clio-kit mcp-server node-hardware |
pandas |
2.0.1 | Data Analysis | CSV data loading and filtering | uvx clio-kit mcp-server pandas |
parallel-sort |
2.0.1 | Computing | Large file sorting | uvx clio-kit mcp-server parallel-sort |
paraview |
2.0.1 | Visualization | Scientific 3D visualization and analysis | uvx clio-kit mcp-server paraview |
parquet |
2.0.1 | Data I/O | Read Parquet file columns | uvx clio-kit mcp-server parquet |
plot |
2.0.1 | Visualization | Generate plots from CSV data | uvx clio-kit mcp-server plot |
slurm |
2.0.1 | HPC | Job submission and management | uvx clio-kit mcp-server slurm |
Agentic Search
Hybrid retrieval engine for scientific corpora — combines lexical (BM25), vector, graph, and scientific search (numeric range, unit matching, formula targeting) over namespaced document collections. DuckDB storage, FastAPI, async job queue, OpenTelemetry tracing, Prometheus metrics.
# Start the search API server
uvx clio-kit search serve
# Index documents from a namespace
uvx clio-kit search index --namespace local_fs
# Query with scientific operators
uvx clio-kit search query --namespace local_fs --q "pressure between 190 and 360 kPa"
# List indexed documents
uvx clio-kit search list --namespace local_fs
API endpoints: /query, /jobs/index, /documents, /health, /metrics — full docs
📖 Usage Examples
HDF5: Scientific Data Analysis
"What datasets are in climate_simulation.h5? Show me the temperature field structure and read the first 100 timesteps."
Tools used: open_file, analyze_dataset_structure, read_partial_dataset, list_attributes
Slurm: HPC Job Management
"Submit simulation.py to Slurm with 32 cores, 64GB memory, 24-hour runtime. Monitor progress and retrieve output when complete."
Tools used: submit_slurm_job, check_job_status, get_job_output
ArXiv: Research Discovery
"Find the latest papers on diffusion models from ArXiv, get details on the top 3, and export citations to BibTeX."
Tools used: search_arxiv, get_paper_details, export_to_bibtex, download_paper_pdf
Pandas: Data Processing
"Load sales_data.csv, clean missing values, compute statistics by region, and save as Parquet with compression."
Tools used: load_data, handle_missing_data, groupby_operations, save_data
Plot: Data Visualization
"Create a line plot showing temperature trends over time from weather.csv with proper axis labels."
Tools used: line_plot, data_info
Agentic Search: Scientific Retrieval
"Find all chunks mentioning pressure above 200 kPa in the local_fs namespace."
CLI: uvx clio-kit search query --namespace local_fs --q "pressure > 200 kPa"
🚨 Troubleshooting
Server Not Found Error
If uvx clio-kit mcp-server <server-name> fails:
# Verify server name is correct
uvx clio-kit mcp-servers
# Common names: hdf5, pandas, slurm, arxiv (not hdf5-mcp, pandas-mcp)
Import Errors or Missing Dependencies
For development or local testing:
cd clio-kit-mcp-servers/hdf5
uv sync --all-extras --dev
uv run hdf5-mcp
uvx Command Not Found
Install uv package manager:
# Linux/macOS
curl -LsSf https://astral.sh/uv/install.sh | sh
# Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
# Or via pip
pip install uv
Team
- Gnosis Research Center (GRC) - Illinois Institute of Technology | Lead
- HDF Group - Data format and library developers | Industry Partner
- University of Utah - Research collaboration | Domain Science Partner
Sponsored By
NSF (National Science Foundation) - Supporting scientific computing research and AI integration initiatives
we welcome more sponsorships. please contact the Principal Investigator
Ways to Contribute
- Submit Issues: Report bugs or request features via GitHub Issues
- Develop New MCPs: Add servers for your research tools (CONTRIBUTING.md)
- Improve Documentation: Help make guides clearer
- Share Use Cases: Tell us how you're using CLIO Kit in your research
Full Guide: CONTRIBUTING.md
Community & Support
- Chat: Zulip Community
- Join: Invitation Link
- Issues: GitHub Issues
- Discussions: GitHub Discussions
- Website: https://docs.iowarp.ai/
- Project: IOWarp Project
Recommended MCP Servers
How it compares
Local node hardware MCP probe, not a cloud-wide monitoring SaaS integration.
FAQ
Who is io.github.iowarp/node-hardware-mcp for?
It is for operators and developers who run workloads on specific machines and want their coding agent to read hardware and system analysis via MCP.
When should I use io.github.iowarp/node-hardware-mcp?
Use it during operate monitoring and performance work when you need the agent to reason about live node hardware while debugging or right-sizing jobs.
How do I add io.github.iowarp/node-hardware-mcp to my agent?
Install clio-kit from PyPI, register io.github.iowarp/node-hardware-mcp with stdio in your MCP config on the target host, and run the agent where node access is appropriate.