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IoWarp MCPs

License: MIT PyPI version Python CI Coverage

Tests WRP Framework

Ruff MyPy uv pip-audit

Collection of MCP servers specifically designed for scientific computing research that enable AI agents and LLMs to interact with data analysis tools, HPC resources, and research datasets through a standardized protocol.

More info at: https://iowarp.github.io/iowarp-mcps/

Quick Installation

All our packages are released on PyPI for easy installation and usage.

Simple Command

# Run any MCP server directly
uvx iowarp-mcps <server-name>

List All MCPs

# See all available MCP servers
uvx iowarp-mcps

Get Started with a Simple Command

# Example: Run the pandas MCP server
uvx iowarp-mcps pandas

# Example: Run the plot MCP server  
uvx iowarp-mcps plot

# Example: Run the slurm MCP server
uvx iowarp-mcps slurm

Available Packages

📦 Package 🔧 System 📋 Description ⚡ Install Command
adios Data I/O Read data using ADIOS2 engine uvx iowarp-mcps adios
arxiv Research Fetch research papers from ArXiv uvx iowarp-mcps arxiv
chronolog Logging Log and retrieve data from ChronoLog uvx iowarp-mcps chronolog
compression Utilities File compression with gzip uvx iowarp-mcps compression
darshan Performance I/O performance trace analysis uvx iowarp-mcps darshan
hdf5 Data I/O List HDF5 files from directories uvx iowarp-mcps hdf5
jarvis Workflow Data pipeline lifecycle management uvx iowarp-mcps jarvis
lmod Environment Environment module management uvx iowarp-mcps lmod
node-hardware System System hardware information uvx iowarp-mcps node-hardware
pandas Data Analysis CSV data loading and filtering uvx iowarp-mcps pandas
parallel-sort Computing Large file sorting simulation uvx iowarp-mcps parallel-sort
parquet Data I/O Read Parquet file columns uvx iowarp-mcps parquet
plot Visualization Generate plots from CSV data uvx iowarp-mcps plot
slurm HPC Job submission simulation uvx iowarp-mcps slurm

Members

Primary Institution:

Collaborating Institutions:

  • 📊 HDF Group - Data format and library developers

Sponsors

NSF Logo NSF (National Science Foundation) - Supporting scientific computing research and AI integration initiatives

Development & Publishing

Testing Development Versions

Development versions are automatically published to TestPyPI on every commit to main:

# Install latest dev version from TestPyPI
uvx --index-url https://test.pypi.org/simple/ iowarp-mcps

Creating Releases

git tag v1.2.3
git push origin v1.2.3

Contributing

We welcome contributions in any form!

Ways to Contribute:

  • Submit Issues: Report any problems or bugs you encounter
  • Request Features: Submit an issue requesting a new MCP server or functionality
  • Develop: Try your hand at developing new MCP servers

Find our comprehensive contribution/development/debugging guide here.

Get Help & Connect

Reach out to us on Zulip: IoWarp-mcp Community Chat


Metadata

Release files for iowarp-mcps 0.3.11

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for iowarp-mcps 0.3.11
File Size Uploaded
iowarp_mcps-0.3.11.tar.gz 9.4 MB Details

Built distribution (wheel)

Table of built distributions (wheels) for iowarp-mcps 0.3.11
File Interpreter ABI Platform
iowarp_mcps-0.3.11-py3-none-any.whl Python 3 none any Details

Total release size: 18.9 MB

Release files / iowarp_mcps-0.3.11.tar.gz

Download URL iowarp_mcps-0.3.11.tar.gz
Size 9.4 MB
Tags Source
SHA-256 checksum
How to use checksums
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Yes
Uploaded via twine/6.1.0 CPython/3.12.9

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PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Aug 24, 2025.

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Release files / iowarp_mcps-0.3.11-py3-none-any.whl

Download URL iowarp_mcps-0.3.11-py3-none-any.whl
Size 9.5 MB
Tags Python 3
SHA-256 checksum
How to use checksums
7a0b3967f769c2643a5ee84119b564718567664c32b246031e5e93531257e9e7
BLAKE2b-256 checksum
How to use checksums
45e332fccf3b02505fd3214df4f4cfa16bd36c22a5ff80a05c7eb4425192e335
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.12.9

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Aug 24, 2025.

Transparency log

Release history Release notifications | RSS feed

This release

0.3.11 This release

2 release files

0.3.7

2 release files

0.3.6

2 release files

0.3.5

2 release files

0.3.4

2 release files

0.3.3

2 release files

0.3.2

2 release files

0.3.1

2 release files

0.3.0

2 release files

0.2.4

2 release files

0.2.3

2 release files

0.2.1

2 release files

0.2.0

2 release files

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