# Math-Physics-ML MCP System
[](https://pypi.org/project/scicomp-math-mcp/)
[](https://pypi.org/project/scicomp-quantum-mcp/)
[](https://pypi.org/project/scicomp-molecular-mcp/)
[](https://pypi.org/project/scicomp-neural-mcp/)
[](https://andylbrummer.github.io/math-mcp/)
[](https://opensource.org/licenses/MIT)
GPU-accelerated [Model Context Protocol](https://modelcontextprotocol.io) 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:
```bash
# Run individual servers
uvx scicomp-math-mcp
uvx scicomp-quantum-mcp
uvx scicomp-molecular-mcp
uvx scicomp-neural-mcp
```
### Installation with pip/uv
```bash
# 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`
```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`:
```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
```python
# 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
```python
# 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
```python
# 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
```python
# 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](https://andylbrummer.github.io/math-mcp/)**
- [Installation Guide](https://andylbrummer.github.io/math-mcp/getting-started/installation)
- [Configuration](https://andylbrummer.github.io/math-mcp/getting-started/configuration)
- [Quick Start Tutorial](https://andylbrummer.github.io/math-mcp/getting-started/quick-start)
- [Architecture Overview](https://andylbrummer.github.io/math-mcp/concepts/architecture)
- [GPU Acceleration](https://andylbrummer.github.io/math-mcp/concepts/gpu-acceleration)
- [API Reference](https://andylbrummer.github.io/math-mcp/api/overview)
## Development
```bash
# 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](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](ARCHITECTURE.md).
## License
MIT License - see [LICENSE](LICENSE) for details.
## Contributing
Contributions are welcome! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
