# math-mcp **Category:** 🏢 Workplace & Productivity **Repository:** https://github.com/andylbrummer/math-mcp **Views:** 0 **Installs:** 0 **Upvotes:** 0 **Directory Page:** https://allmcps.com/mcp/math-mcp ## Description GPU-accelerated MCP server: symbolic algebra, numerical computing, FFT, optimization, linear algebra ## Claude Desktop Quick Installation Heuristic fallback — verify the package name and runner against the repository README before running it. Uses `npx` (confidence: low): ```json "mcpServers": { "math-mcp": { "command": "npx", "args": ["-y","math-mcp"] } } ``` ## Documentation & README # Math-Physics-ML MCP System [![PyPI - Math MCP](https://img.shields.io/pypi/v/scicomp-math-mcp?label=scicomp-math-mcp)](https://pypi.org/project/scicomp-math-mcp/) [![PyPI - Quantum MCP](https://img.shields.io/pypi/v/scicomp-quantum-mcp?label=scicomp-quantum-mcp)](https://pypi.org/project/scicomp-quantum-mcp/) [![PyPI - Molecular MCP](https://img.shields.io/pypi/v/scicomp-molecular-mcp?label=scicomp-molecular-mcp)](https://pypi.org/project/scicomp-molecular-mcp/) [![PyPI - Neural MCP](https://img.shields.io/pypi/v/scicomp-neural-mcp?label=scicomp-neural-mcp)](https://pypi.org/project/scicomp-neural-mcp/) [![Documentation](https://img.shields.io/badge/docs-GitHub%20Pages-blue)](https://andylbrummer.github.io/math-mcp/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) GPU-accelerated [Model Context Protocol](https://modelcontextprotocol.io) servers for computational mathematics, physics simulations, and machine learning. ## 📚 Documentation **[View Full Documentation →](https://andylbrummer.github.io/math-mcp/)** | Guide | Description | |-------|-------------| | [Installation](https://andylbrummer.github.io/math-mcp/getting-started/installation) | Setup instructions for pip, uv, and uvx | | [Configuration](https://andylbrummer.github.io/math-mcp/getting-started/configuration) | Claude Desktop & Claude Code setup | | [Quick Start](https://andylbrummer.github.io/math-mcp/getting-started/quick-start) | Get running in 5 minutes | | [API Reference](https://andylbrummer.github.io/math-mcp/api/overview) | Complete tool documentation | | [Visual Demos](https://andylbrummer.github.io/math-mcp/demos/) | Interactive physics simulations | ## About This system enables AI assistants to perform real scientific computing — from solving differential equations to running molecular dynamics simulations.
Double-Slit Interference
Quantum Wave Mechanics
Double-slit interference pattern from solving the time-dependent Schrödinger equation 		Galaxy Collision
N-Body Dynamics
Galaxy merger simulation using gravitational N-body calculations
Bragg Scattering
Crystal Diffraction
Bragg scattering from a hexagonal (graphene-like) lattice 		Triple-Slit
Multi-Slit Interference
Complex interference patterns from three coherent sources
## 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") ``` ## Development ```bash # 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](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.