Which integrations are available for this server?

Hosts the source code repository and documentation for the Math-Physics-ML MCP System. Enables exporting trained neural network models to [ONNX](/mcp/servers/integrations/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.

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.

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")

Documentation

Full documentation is available at andylbrummer.github.io/math-mcp

Development

# Clone the repository git clone https://github.com/andylbrummer/math-mcp.git cd math-mcp # Install dependencies uv sync --all-extras # 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

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.

Math-Physics-ML MCP System

Math-Physics-ML MCP System

Overview

Quick Start

Installation with uvx (Recommended)

Installation with pip/uv

Configuration

Claude Desktop

Claude Code

Usage Examples

Math MCP

Quantum MCP

Molecular MCP

Neural MCP

Documentation

Development

Performance

Architecture

License

Contributing

Resources

New MCP Servers

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