Skip to main content

scicomp-molecular-mcp

mcp-name: io.github.andylbrummer/molecular-mcp

MCP server for classical molecular dynamics simulations.

Overview

This server provides tools for molecular dynamics simulations with support for various ensembles and interaction potentials:

  • Particle systems - Create and manage systems of interacting particles
  • Interaction potentials - Lennard-Jones and Coulomb interactions
  • Ensembles - NVE (microcanonical), NVT (canonical), NPT (isothermal-isobaric)
  • Analysis tools - Radial distribution functions, mean squared displacement, phase transitions
  • Visualization - Trajectory rendering and density field visualization
  • GPU acceleration - Optional CUDA acceleration for large systems

Installation & Usage

# Run directly with uvx (no installation required)
uvx scicomp-molecular-mcp

# Or install with pip
pip install scicomp-molecular-mcp

# With GPU support
pip install scicomp-molecular-mcp[gpu]

# Run as command
scicomp-molecular-mcp

Available Tools

System Setup

  • create_particles - Initialize particle system with temperature
  • add_potential - Add Lennard-Jones or Coulomb interactions

Simulation

  • run_md - NVE ensemble (constant energy, volume)
  • run_nvt - NVT ensemble (constant temperature, volume)
  • run_npt - NPT ensemble (constant temperature, pressure)
  • get_trajectory - Retrieve simulation trajectory data

Analysis

  • compute_rdf - Radial distribution function analysis
  • compute_msd - Mean squared displacement
  • analyze_temperature - Thermodynamic properties
  • detect_phase_transition - Identify phase transitions
  • density_field - Compute density field visualization

Visualization

  • render_trajectory - Animate particle trajectories

Configuration

Enable GPU acceleration with environment variable:

MCP_USE_GPU=1 scicomp-molecular-mcp

Examples

🎬 Visual Demos

Spectacular animated demonstrations:

Run demos with Claude:

claude -p "Simulate two galaxies colliding" \
  --allowedTools "mcp__molecular-mcp__*"

📖 Code Examples

Practical tutorials in EXAMPLES.md:

  • Simple liquid simulation with Lennard-Jones
  • Temperature & pressure control
  • Diffusion coefficient calculation
  • Ionic systems with Coulomb interactions

📚 Full Documentation

See the API documentation for complete reference.

Part of Math-Physics-ML MCP System

Part of a comprehensive system for scientific computing. See the documentation for the complete ecosystem.

Metadata

Release files for scicomp-molecular-mcp 0.1.6

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

Source distribution (sdist)

Source distribution for scicomp-molecular-mcp 0.1.6
File Size Uploaded Provenance
scicomp_molecular_mcp-0.1.6.tar.gz 13.6 kB With provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for scicomp-molecular-mcp 0.1.6
File Interpreter ABI Platform
scicomp_molecular_mcp-0.1.6-py3-none-any.whl Python 3 none any Details

Total release size: 23.5 kB

Release files / scicomp_molecular_mcp-0.1.6.tar.gz

Download URL scicomp_molecular_mcp-0.1.6.tar.gz
Size 13.6 kB
Tags Source
SHA-256 checksum
How to use checksums
f1b564939ae1849f4d67a6242589ebb4c266716640b3fe74526490771abdc6e9
BLAKE2b-256 checksum
How to use checksums
b49fa90c4fc85a3cb69b470ef8b8a88b4f1fc6592ef3981f3abe37094885816d
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

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 Jan 5, 2026.

Transparency log

Release files / scicomp_molecular_mcp-0.1.6-py3-none-any.whl

Download URL scicomp_molecular_mcp-0.1.6-py3-none-any.whl
Size 9.9 kB
Tags Python 3
SHA-256 checksum
How to use checksums
616862982674d3959d7fc8dc981f343ccb6e564c0841ba2d1647c4c49eb0147f
BLAKE2b-256 checksum
How to use checksums
7de581a0e20a1d64c7cf1a02a880767fd71f1a81ec79a4d31c97d05ba56fb10a
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

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 Jan 5, 2026.

Transparency log

Security for scicomp-molecular-mcp 0.1.6

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.

Full provenance

All 2 files in this release have been uploaded with provenance metadata.

Select from 2 files

Attestation for scicomp_molecular_mcp-0.1.6.tar.gz

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 Jan 5, 2026.

Transparency log

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page