Power your agent
The publisher's own command. Not a PlugMCP endpoint.
Host a copy with our keys and logs.
uvx scicomp-quantum-mcp==0.1.6Claude Code has no install link for a local command. Copy the command below and run it in a terminal.
claude mcp add io.github.andylbrummer-quantum-mcp -- uvx scicomp-quantum-mcp==0.1.6Run in a terminal.
{ "mcpServers": { "io.github.andylbrummer-quantum-mcp": { "command": "uvx", "args": [ "scicomp-quantum-mcp==0.1.6" ] } }}claude_desktop_config.json
Remote URL or a command on your machine? How the two installs differ
Run on PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
LaunchGPU-accelerated Model Context Protocol servers for computational mathematics, physics simulations, and machine learning.
This system enables AI assistants to perform real scientific computing — from solving differential equations to running molecular dynamics simulations.
This system provides 4 specialized MCP servers that bring scientific computing capabilities to AI assistants like Claude:
Key Features:
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
# 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]
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"]
}
}
}
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.
# 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)
# 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
)
# 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)
# 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")
# 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 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.
GPU acceleration provides significant speedups for compute-intensive operations:
For technical details about the system architecture, see ARCHITECTURE.md.
MIT License - see LICENSE for details.
Contributions are welcome! Please see CONTRIBUTING.md for guidelines.
© 2026 MCP Directory.
GPU-accelerated MCP server: quantum mechanics, wavefunction evolution, Schrodinger simulations
{ "mcpServers": { "io.github.andylbrummer-quantum-mcp": { "command": "uvx", "args": [ "scicomp-quantum-mcp==0.1.6" ] } }}Tools
No tool list yet. Remote servers get one after the first health check.
FAQ
Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.
No required environment variable is listed on this record.
Quantum runs locally over stdio.
Last checked 2026-11-09. GPU-accelerated MCP server: quantum mechanics, wavefunction evolution, Schrodinger simulations