Math MCP Server

Math-Physics-ML MCP System

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

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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:

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:

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.