Provides a backend for scientific computing using Julia, supporting numeric computation and plotting.

Provides a backend for scientific computing using GNU Octave, supporting numeric computation and plotting.

Provides a backend for scientific computing using Python with scientific libraries (e.g., NumPy, SciPy, SymPy, pandas), supporting symbolic, numeric, and plotting capabilities.

Provides a backend for scientific computing using SageMath, supporting symbolic, numeric, and plotting capabilities.

Provides a backend for scientific computing using Wolfram Mathematica, supporting symbolic, numeric, plotting, and image/audio output.

1. Click on "Install Server".

2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.

3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@scicompute-mcp solve the equation x^2 + 2x - 8 = 0"

That's it! The server will respond to your query, and you can continue using it as needed.

Here is a step-by-step guide with screenshots.

# Install and run with uvx (auto-manages environment)
uvx scicompute-mcp

# Or install with pip
pip install scicompute-mcp
scicompute-mcp

# Method 1: Use --break-system-packages (quick)
pip install --break-system-packages scicompute-mcp

# Method 2: Use pipx (recommended for CLI tools)
sudo apt install -y pipx
pipx install scicompute-mcp

# Method 3: Use China mirror (faster in China)
pip install --break-system-packages -i https://pypi.tuna.tsinghua.edu.cn/simple scicompute-mcp

echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.bashrc
source ~/.bashrc

# With Mathematica support
pip install scicompute-mcp[mathematica]

# With Octave support
pip install scicompute-mcp[octave]

# With all backends
pip install scicompute-mcp[all]

┌───────────────────────────────────────────────────────────────────────────┐
│ MCP Server (Python 3.10+) │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌────────────────────┐ │
│ │ Mathematica │ │ Octave │ │ MATLAB │ │ py_scientific │ │
│ │ Backend │ │ Backend │ │ Backend │ │ (same Python env) │ │
│ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ └────────────────────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Wolfram │ │ octave │ │ MATLAB │ │ R │ sage │
│ │ Kernel │ │ process │ │ process │ │ process │ process │
│ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ │
│ │ │ │ │ │ │
│ ▼ ▼ ▼ ▼ ▼ │
│ Independent Independent Independent Independent conda │
│ (official) (apt/brew) (official) (apt/brew) env │
└───────────────────────────────────────────────────────────────────────────┘

# Configure mirror (optional, recommended for users in China)
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/

# Create SageMath environment
conda create -n sage python=3.11 -y
conda install -n sage -c conda-forge sage -y

# Method A: Environment variable (recommended)
export SAGE_PATH="$HOME/miniconda3/envs/sage/bin/sage"

# Method B: Modify SAGE_PATH in code
# Edit src/scicompute_mcp/backends/sage.py

# Ubuntu/Debian
sudo apt install r-base

# macOS
brew install r

# Windows: Download from CRAN

# Ubuntu/Debian
sudo apt install octave gnuplot

# macOS
brew install octave gnuplot

# Windows: Download Octave installer

# Install Julia (recommended: use juliaup)
curl -fsSL https://install.julialang.org | sh

# Or download from https://julialang.org/downloads/

# Install Python package
pip install juliacall

export MATHEMATICA_KERNEL_PATH="/usr/local/Wolfram/Wolfram/14.3/Executables/WolframKernel"

{
 "mcpServers": {
 "scicompute": {
 "command": "uvx",
 "args": ["scicompute-mcp"]
 }
 }
}

{
 "mcpServers": {
 "scicompute": {
 "command": "uvx",
 "args": ["scicompute-mcp"]
 }
 }
}

{
 "$schema": "https://opencode.ai/config.json",
 "mcp": {
 "scicompute": {
 "type": "local",
 "command": ["/home/username/.local/bin/scicompute-mcp"]
 }
 }
}

opencode mcp list

opencode mcp add
# Name: scicompute
# Type: Local
# Command: /home/username/.local/bin/scicompute-mcp

{
 "mcpServers": {
 "scicompute": {
 "type": "stdio",
 "command": "/home/username/.local/bin/scicompute-mcp"
 }
 }
}

{
 "mcpServers": {
 "scicompute": {
 "command": "/path/to/.venv/bin/python",
 "args": ["-m", "scicompute_mcp.server"]
 }
 }
}

# Plot with Octave
compute("x = 0:0.1:10; y = sin(x); plot(x, y)", "octave")

# Symbolic computation with SageMath
compute("integrate(sin(x), x)", "sage")
compute("diff(x^3 * exp(x), x)", "sage")

# Mathematica
compute("Plot[Sin[x], {x, 0, 2 Pi}]", "mathematica")
compute("Integrate[x^2, x]", "mathematica")

# R Statistics
compute("mean(rnorm(1000))", "r")
compute("hist(rnorm(1000))", "r")

# Python Scientific
compute("sp.integrate(sp.sin(sp.Symbol('x')), sp.Symbol('x'))", "py_scientific")

# Julia
compute("plot(rand(10))", "julia")
compute("sqrt(2.0)", "julia")

# MATLAB
compute("plot(1:10, rand(1,10))", "matlab")
compute("[V,D] = eig(magic(3))", "matlab")

stop() # List running backends (does NOT stop any)
stop("octave") # Stop specific backend
stop("ALL") # Stop all running backends

# List all backends with documentation
doc()

# Get URLs for a specific backend
doc(backend="mathematica")
doc(backend="numpy")

# Get URL for a specific function
doc(backend="mathematica", symbol="Plot3D")
doc(backend="octave", symbol="linspace")

Plot sin(x) from 0 to 2π

Calculate ∫x²dx from 0 to 1

Solve x² - 4 = 0

Look up NDSolve documentation

# Get documentation URL
doc(backend="mathematica", symbol="Plot3D")

# Then fetch the documentation
Task(
 description="Fetch Plot3D docs",
 prompt="Fetch Mathematica documentation for Plot3D from the URL returned by doc tool",
 subagent_type="general"
)

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