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andylbrummer

Math-Physics-ML MCP System

by andylbrummer
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Math-Physics-ML MCP System

PyPI - Math MCP PyPI - Quantum MCP PyPI - Molecular MCP PyPI - Neural MCP Documentation License: MIT

GPU-accelerated Model Context Protocol servers for computational mathematics, physics simulations, and machine learning.

📚 Documentation

View Full Documentation →

Guide

Description

Installation

Setup instructions for pip, uv, and uvx

Configuration

Claude Desktop & Claude Code setup

Quick Start

Get running in 5 minutes

API Reference

Complete tool documentation

Visual Demos

Interactive physics simulations

Related MCP server: FLASK-tools

About

This system enables AI assistants to perform real scientific computing — from solving differential equations to running molecular dynamics simulations.

Overview

This system provides 4 specialized MCP servers that bring scientific computing capabilities to AI assistants like Claude:

Server

Description

Tools

Math MCP

Symbolic algebra (SymPy) + numerical computing

14

Quantum MCP

Wave mechanics & Schrodinger simulations

12

Molecular MCP

Classical molecular dynamics

15

Neural MCP

Neural network training & evaluation

16

Key Features:

  • GPU acceleration with automatic CUDA detection (10-100x speedup)

  • Async task support for long-running simulations

  • Cross-MCP workflows via URI-based data sharing

  • Progressive discovery for efficient tool exploration

Quick Start

Installation with uvx (Recommended)

Run any MCP server directly without installation:

# Run individual servers
uvx scicomp-math-mcp
uvx scicomp-quantum-mcp
uvx scicomp-molecular-mcp
uvx scicomp-neural-mcp

Installation with pip/uv

# Install individual servers
pip install scicomp-math-mcp
pip install scicomp-quantum-mcp
pip install scicomp-molecular-mcp
pip install scicomp-neural-mcp

# Or install all at once
pip install scicomp-math-mcp scicomp-quantum-mcp scicomp-molecular-mcp scicomp-neural-mcp

# With GPU support (requires CUDA)
pip install scicomp-math-mcp[gpu] scicomp-quantum-mcp[gpu] scicomp-molecular-mcp[gpu] scicomp-neural-mcp[gpu]

Configuration

Claude Desktop

Add to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "math-mcp": {
      "command": "uvx",
      "args": ["scicomp-math-mcp"]
    },
    "quantum-mcp": {
      "command": "uvx",
      "args": ["scicomp-quantum-mcp"]
    },
    "molecular-mcp": {
      "command": "uvx",
      "args": ["scicomp-molecular-mcp"]
    },
    "neural-mcp": {
      "command": "uvx",
      "args": ["scicomp-neural-mcp"]
    }
  }
}

Claude Code

Add to your project's .mcp.json:

{
  "mcpServers": {
    "math-mcp": {
      "command": "uvx",
      "args": ["scicomp-math-mcp"]
    },
    "quantum-mcp": {
      "command": "uvx",
      "args": ["scicomp-quantum-mcp"]
    }
  }
}

Or configure globally in ~/.claude/settings.json.

Usage Examples

Math MCP

# Solve equations symbolically
symbolic_solve(equations="x**3 - 6*x**2 + 11*x - 6")
# Result: [1, 2, 3]

# Compute derivatives
symbolic_diff(expression="sin(x)*exp(-x**2)", variable="x")
# Result: cos(x)*exp(-x**2) - 2*x*sin(x)*exp(-x**2)

# GPU-accelerated matrix operations
result = matrix_multiply(a=matrix_a, b=matrix_b, use_gpu=True)

Quantum MCP

# Create a Gaussian wave packet
psi = create_gaussian_wavepacket(
    grid_size=[256],
    position=[64],
    momentum=[2.0],
    width=5.0
)

# Solve time-dependent Schrodinger equation
simulation = solve_schrodinger(
    potential=barrier_potential,
    initial_state=psi,
    time_steps=1000,
    dt=0.1,
    use_gpu=True
)

Molecular MCP

# Create particle system
system = create_particles(
    n_particles=1000,
    box_size=[20, 20, 20],
    temperature=1.5
)

# Add Lennard-Jones potential
add_potential(system_id=system, potential_type="lennard_jones")

# Run MD simulation
trajectory = run_nvt(system_id=system, n_steps=100000, temperature=1.0)

# Analyze diffusion
msd = compute_msd(trajectory_id=trajectory)

Neural MCP

# Define model
model = define_model(architecture="resnet18", num_classes=10, pretrained=True)

# Load dataset
dataset = load_dataset(dataset_name="CIFAR10", split="train")

# Train
experiment = train_model(
    model_id=model,
    dataset_id=dataset,
    epochs=50,
    batch_size=128,
    use_gpu=True
)

# Export for deployment
export_model(model_id=model, format="onnx", output_path="model.onnx")

Development

# Clone the repository
git clone https://github.com/andylbrummer/math-mcp.git
cd math-mcp

# Install dependencies
uv sync --all-extras

# Install MCP servers in editable mode (required for entry points)
uv pip install --python .venv/bin/python \
  -e servers/math-mcp \
  -e servers/quantum-mcp \
  -e servers/molecular-mcp \
  -e servers/neural-mcp

# Run tests
uv run pytest -m "not gpu"  # CPU only
uv run pytest               # All tests (requires CUDA)

# Run with coverage
uv run pytest --cov=shared --cov=servers

Note: The editable install step is required because uv sync doesn't install entry point scripts for workspace packages. After this step, you can run servers directly with uv run scicomp-math-mcp.

See CONTRIBUTING.md for development guidelines.

Performance

GPU acceleration provides significant speedups for compute-intensive operations:

MCP

Operation

CPU

GPU

Speedup

Math

Matrix multiply (4096x4096)

2.1s

35ms

60x

Quantum

2D Schrodinger (512x512, 1000 steps)

2h

2min

60x

Molecular

MD (100k particles, 10k steps)

1h

30s

120x

Neural

ResNet18 training (1 epoch)

45min

30s

90x

Architecture

For technical details about the system architecture, see ARCHITECTURE.md.

License

MIT License - see LICENSE for details.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

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Maintenance

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