CLIO Hdf5 MCP Server
io.github.iowarp/hdf5-mcp
HDF5 MCP server for AI agents to analyze and explore scientific data with 27 specialized tools.
What is the CLIO Hdf5 MCP server?
The HDF5 MCP server is part of CLIO Kit, enabling AI agents to read, analyze, and explore HDF5 scientific data files through natural language. It provides 27 tools for dataset inspection, partial reading, attribute access, and AI-powered insights with caching and streaming support.
This server gives AI agents direct access to HDF5 files commonly used in scientific computing and HPC environments. You can ask an agent to explore file structure, read specific datasets, analyze metadata, and generate insights—all without manual scripting. It's optimized for large scientific datasets with streaming and caching capabilities.
How to install CLIO Hdf5
Copy-paste configuration for popular MCP clients.
Tools & capabilities
Tools this server exposes to the agent.
open_file— Open and access HDF5 fileslist_datasets— List all datasets in an HDF5 fileanalyze_dataset_structure— Analyze the structure and metadata of datasetsread_partial_dataset— Read a subset of data from a datasetlist_attributes— List and retrieve file and dataset attributesget_dataset_info— Get detailed information about a datasetsearch_datasets— Search for datasets by name or pattern
Use cases
- Explore structure and metadata of large climate or simulation HDF5 files
- Read specific timesteps or regions from scientific datasets without loading entire files
- Extract and analyze temperature, pressure, or other field data from HDF5 archives
- Generate AI insights about dataset contents and relationships
- Integrate HDF5 data exploration into automated scientific workflows
CLIO Hdf5 MCP server FAQ
It's an MCP server that gives AI agents like Claude and Cursor the ability to read, explore, and analyze HDF5 scientific data files through natural language commands, with 27 specialized tools for dataset inspection and analysis.
Yes, CLIO Kit is open-source under the BSD-3-Clause license and available on PyPI.
Add to ~/.cursor/mcp.json: {"hdf5-mcp": {"command": "clio-kit", "args": ["mcp-server", "hdf5"]}}. First install via `uv tool install 'clio-kit==2.4.3'`.
Edit claude_desktop_config.json and add: {"hdf5-mcp": {"command": "clio-kit", "args": ["mcp-server", "hdf5"]}}
No authentication is required. The server accesses local HDF5 files on your system.
Python 3.10 or higher is required to run CLIO Kit and the HDF5 MCP server.
README (reference)
Source of truth, from the repository.
CLIO Kit
<!-- mcp-name: io.github.iowarp/adios-mcp --> <!-- mcp-name: io.github.iowarp/arxiv-mcp --> <!-- mcp-name: io.github.iowarp/chronolog-mcp --> <!-- mcp-name: io.github.iowarp/compression-mcp --> <!-- mcp-name: io.github.iowarp/darshan-mcp --> <!-- mcp-name: io.github.iowarp/geo-mcp --> <!-- mcp-name: io.github.iowarp/geojson-mcp --> <!-- mcp-name: io.github.iowarp/hdf5-mcp --> <!-- mcp-name: io.github.iowarp/jarvis-mcp --> <!-- mcp-name: io.github.iowarp/lmod-mcp --> <!-- mcp-name: io.github.iowarp/ndp-mcp --> <!-- mcp-name: io.github.iowarp/node-hardware-mcp --> <!-- mcp-name: io.github.iowarp/pandas-mcp --> <!-- mcp-name: io.github.iowarp/parallel-sort-mcp --> <!-- mcp-name: io.github.iowarp/paraview-mcp --> <!-- mcp-name: io.github.iowarp/parquet-mcp --> <!-- mcp-name: io.github.iowarp/plot-mcp --> <!-- mcp-name: io.github.iowarp/sac-mcp --> <!-- mcp-name: io.github.iowarp/scientific-catalog-mcp --> <!-- mcp-name: io.github.iowarp/seismic-mcp --> <!-- mcp-name: io.github.iowarp/slurm-mcp --> <!-- mcp-name: io.github.iowarp/spack-mcp --> <!-- mcp-name: io.github.iowarp/terrain-mcp --> <!-- mcp-name: io.github.iowarp/web-mcp -->CLIO Kit - Part of the IoWarp platform's tooling layer for AI agents. A comprehensive collection of tools, skills, plugins, and extensions. It ships 22 Model Context Protocol (MCP) servers for scientific computing and enables AI agents to interact with HPC resources, scientific data formats, and research datasets.
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Developed by <img src="https://grc.iit.edu/img/logo.png" alt="GRC Logo" width="18" height="18"> 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. 22 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, BSD-3-Clause licensed, and live-tested in real HPC environments.
🚀 Quick Installation
One Command for Any Server
# Install the released CLI into its own persistent tool environment
uv tool install 'clio-kit==2.4.3'
# If uv reports that its executable directory is not on PATH:
uv tool update-shell
# List all 23 available MCP servers
clio-kit mcp-servers
# Run any installed server
clio-kit mcp-server hdf5
clio-kit mcp-server pandas
clio-kit mcp-server slurm
# Agentic search — hybrid retrieval for scientific corpora
clio-kit search serve # Start search API server
clio-kit search query --namespace local_fs --q "pressure > 200 kPa"
# AI prompts also available
clio-kit prompts # List all prompts
clio-kit prompt code-coverage-prompt # Use a prompt
uv tool install keeps CLIO Kit in a persistent, isolated tool environment.
Use uvx --from 'clio-kit==2.4.3' clio-kit ... only for a temporary, one-shot
invocation.
Released clio-kit wheels execute each embedded MCP server from that server's
shipped uv.lock. The launcher uses a source-and-lock-addressed environment
under the user cache, installs only production dependencies, and refuses to
resolve an embedded server whose lock is missing. The --branch launcher
option is an explicit development path and is not an immutable
release-artifact path.
The root wheel also ships machine-readable user contracts for the locked
JARVIS, SLURM, Spack, and Scientific Catalog servers. These artifacts are generated from real stdio
tools/list exchanges and include canonical SHA-256 digests for downstream
federation gates:
clio-kit mcp-contracts
clio-kit mcp-contract clio-kit-jarvis-user-v3.5
clio-kit mcp-contract clio-kit-slurm-user-v3
clio-kit mcp-contract clio-kit-spack-user-v2.1
clio-kit mcp-contract clio-kit-scientific-catalog-user-v1.1
<details>
<summary><b>Install in Cursor</b></summary>
Add to your Cursor ~/.cursor/mcp.json:
{
"mcpServers": {
"hdf5-mcp": {
"command": "clio-kit",
"args": ["mcp-server", "hdf5"]
},
"pandas-mcp": {
"command": "clio-kit",
"args": ["mcp-server", "pandas"]
},
"slurm-mcp": {
"command": "clio-kit",
"args": ["mcp-server", "slurm"]
}
}
}
See Cursor MCP docs for more info.
</details> <details> <summary><b>Install in Claude Code</b></summary># Add HDF5 MCP
claude mcp add hdf5-mcp -- clio-kit mcp-server hdf5
# Add Pandas MCP
claude mcp add pandas-mcp -- clio-kit mcp-server pandas
# Add Slurm MCP
claude mcp add slurm-mcp -- clio-kit mcp-server slurm
See Claude Code MCP docs for more info.
</details> <details> <summary><b>Install in VS Code</b></summary>Add to your VS Code MCP config:
"mcp": {
"servers": {
"hdf5-mcp": {
"type": "stdio",
"command": "clio-kit",
"args": ["mcp-server", "hdf5"]
},
"pandas-mcp": {
"type": "stdio",
"command": "clio-kit",
"args": ["mcp-server", "pandas"]
}
}
}
See VS Code MCP docs for more info.
</details> <details> <summary><b>Install in Claude Desktop</b></summary>Edit claude_desktop_config.json:
{
"mcpServers": {
"hdf5-mcp": {
"command": "clio-kit",
"args": ["mcp-server", "hdf5"]
},
"arxiv-mcp": {
"command": "clio-kit",
"args": ["mcp-server", "arxiv"]
}
}
}
See Claude Desktop MCP docs for more info.
</details>Available Packages
The version below is each MCP server's agent-facing contract version, not the
containing clio-kit wheel version. JARVIS 3.4 and SLURM 3.0 have contracts
redesigned for agent use. Spack is at 2.1, while the other
contracts retain their existing 2.x identities until a focused upgrade.
The Spack install contract makes concretization explicit: reuse=true passes
spack install --reuse, while reuse=false passes spack install --fresh.
Agents should discover first, install only when needed, then pass the exact
spack_locate result to JARVIS for runtime loading. A find with no installed
match is normal typed data (count=0, packages=[]); locate reports the
distinct not_installed semantic, while real Spack failures remain errors.
| 📦 Package | 📌 Ver | 🔧 System | 📋 Description | ⚡ Install Command |
|---|---|---|---|---|
adios | 2.2.3 | Data I/O | Read data using ADIOS2 engine | clio-kit mcp-server adios |
arxiv | 2.2.3 | Research | Fetch research papers from ArXiv | clio-kit mcp-server arxiv |
chronolog | 2.0.1 | Logging | Log and retrieve data from ChronoLog | clio-kit mcp-server chronolog |
compression | 2.2.3 | Utilities | File compression with gzip | clio-kit mcp-server compression |
darshan | 2.2.3 | Performance | I/O performance trace analysis | clio-kit mcp-server darshan |
geo | 2.2.3 | Geospatial | Render GeoJSON vector layers with basemaps | clio-kit mcp-server geo |
geojson | 2.2.3 | Geospatial | Inspect, validate, and summarize GeoJSON | clio-kit mcp-server geojson |
hdf5 | 2.2.3 | Data I/O | HPC-optimized scientific data with 27 tools, AI insights, caching, streaming | clio-kit mcp-server hdf5 |
jarvis | 3.7.2 | Workflow | Durable pipeline, bounded package discovery, progress, artifact, and service-runtime management | clio-kit mcp-server jarvis |
lmod | 2.2.3 | Environment | Environment module management | clio-kit mcp-server lmod |
ndp | 2.2.3 | Data Protocol | Search and discover datasets across CKAN instances | clio-kit mcp-server ndp |
node-hardware | 2.2.3 | System | System hardware information | clio-kit mcp-server node-hardware |
pandas | 2.2.3 | Data Analysis | CSV data loading and filtering | clio-kit mcp-server pandas |
parallel-sort | 2.2.3 | Computing | Large file sorting | clio-kit mcp-server parallel-sort |
paraview | 2.2.3 | Visualization | Scientific 3D visualization and analysis | clio-kit mcp-server paraview |
parquet | 2.2.3 | Data I/O | Read Parquet file columns | clio-kit mcp-server parquet |
plot | 2.2.3 | Visualization | Generate plots from CSV data | clio-kit mcp-server plot |
sac | 2.2.3 | Seismology | Analyze SAC waveforms and archives | clio-kit mcp-server sac |
seismic | 2.2.3 | Seismology | Analyze earthquake catalogs and sequences | clio-kit mcp-server seismic |
scientific-catalog | 1.1.2 | Discovery | Operator-owned scientific dataset discovery | clio-kit mcp-server scientific-catalog |
slurm | 3.0.0 | HPC | Job submission and management | clio-kit mcp-server slurm |
spack | 2.1.0 | Package Management | Structured package discovery, installation, and location | clio-kit mcp-server spack |
terrain | 2.2.3 | Geospatial | Analyze DEMs and terrain point clouds | clio-kit mcp-server terrain |
web | 2.1.0 | Web | Synchronous search plus durable, queryable, cancellable URL, DOI, and document fetch tasks | clio-kit mcp-server web |
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
clio-kit search serve
# Index documents from a namespace
clio-kit search index --namespace local_fs
# Query with scientific operators
clio-kit search query --namespace local_fs --q "pressure between 190 and 360 kPa"
# List indexed documents
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: clio-kit search query --namespace local_fs --q "pressure > 200 kPa"
🚨 Troubleshooting
<details> <summary><b>Server Not Found Error</b></summary>If clio-kit mcp-server <server-name> fails:
# Verify server name is correct
clio-kit mcp-servers
# Common names: hdf5, pandas, slurm, arxiv (not hdf5-mcp, pandas-mcp)
</details>
<details>
<summary><b>Import Errors or Missing Dependencies</b></summary>
For development or local testing:
cd clio-kit-mcp-servers/hdf5
uv sync --all-extras --dev
uv run hdf5-mcp
</details>
<details>
<summary><b>uv or clio-kit Command Not Found</b></summary>
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
Then install CLIO Kit persistently and expose uv's tool directory:
uv tool install 'clio-kit==2.4.3'
uv tool update-shell
</details>
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
<img src="https://www.nsf.gov/themes/custom/nsf_theme/components/molecules/logo/logo-desktop.png" alt="NSF Logo" width="24" height="24"> 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
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