Hosts the source code repository and documentation for the Math-Physics-ML MCP System.
Enables exporting trained neural network models to ONNX format for deployment through the Neural MCP server.
Distributes four specialized scientific computing MCP servers (math, quantum, molecular, and neural) as installable Python packages.
Used for testing the MCP servers with coverage support for both CPU and GPU operations.
Implements the MCP servers and provides the runtime environment for scientific computing operations.
Provides symbolic algebra capabilities including equation solving, differentiation, integration, and matrix operations through the Math MCP server.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Math-Physics-ML MCP Systemsolve the Schrödinger equation for a harmonic oscillator potential"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Math-Physics-ML MCP System
GPU-accelerated Model Context Protocol servers for computational mathematics, physics simulations, and machine learning.
📚 Documentation
Guide | Description |
Setup instructions for pip, uv, and uvx | |
Claude Desktop & Claude Code setup | |
Get running in 5 minutes | |
Complete tool documentation | |
Interactive physics simulations |
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-mcpInstallation 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=serversNote: The editable install step is required because
uv syncdoesn't install entry point scripts for workspace packages. After this step, you can run servers directly withuv 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.