$Math MCP logo$

Math MCP

GPU-accelerated MCP server: symbolic algebra, numerical computing, FFT, optimization, linear algebra

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

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

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

Math-Physics-ML MCP System

$PyPI - Math MCP$

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

📚 Documentation

View Full Documentation →

Guide	Description
Installation	Setup instructions for pip, uv, and uvx
Configuration	Claude Desktop & Claude Code setup
Quick Start	Get running in 5 minutes
API Reference	Complete tool documentation
Visual 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

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:

config.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 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.

License

MIT License - see LICENSE for details.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Read the full README →View source on GitHub →

Related MCP Servers

View all in Data Science Tools View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Last commit

4mo ago

Most recent push to the default branch.

Directory activity

1 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Math MCP

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "math-mcp": { "command": "uvx", "args": ["scicomp-math-mcp"] } }

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Technical Specs & Signals

CategoryData Science Tools

3/7 checks healthy over the last 45d

37Quality signal: Fair · 37/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools16/30

Adoption & activity2/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 5d ago via OSV.dev · scicomp-math-mcp (PyPI)

★ Featured

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More in 🧮 Data Science Tools →Best MCP servers for Data Science →Alternatives to Math MCP →Install in Claude Desktop Install in Cursor Install in VS Code Setup guides for all 13 MCP clients

$Math MCP logo$

Math MCP

GPU-accelerated MCP server: symbolic algebra, numerical computing, FFT, optimization, linear algebra

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

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

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

Math-Physics-ML MCP System

$PyPI - Math MCP$

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

📚 Documentation

View Full Documentation →

Guide	Description
Installation	Setup instructions for pip, uv, and uvx
Configuration	Claude Desktop & Claude Code setup
Quick Start	Get running in 5 minutes
API Reference	Complete tool documentation
Visual 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

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:

config.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 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.

License

MIT License - see LICENSE for details.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Read the full README →View source on GitHub →

Related MCP Servers

View all in Data Science Tools View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Last commit

4mo ago

Most recent push to the default branch.

Directory activity

1 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Math MCP

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "math-mcp": { "command": "uvx", "args": ["scicomp-math-mcp"] } }

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Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

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[![AllMCPs](https://allmcps.com/api/badge/math-mcp?style=directory)](https://allmcps.com/mcp/math-mcp)

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Technical Specs & Signals

CategoryData Science Tools

3/7 checks healthy over the last 45d

37Quality signal: Fair · 37/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools16/30

Adoption & activity2/15

Community engagement0/10

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 5d ago via OSV.dev · scicomp-math-mcp (PyPI)

★ Featured

AllMCPs Server

Explore Server →

Own this project?

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

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Explore more

More in 🧮 Data Science Tools →Best MCP servers for Data Science →Alternatives to Math MCP →Install in Claude Desktop Install in Cursor Install in VS Code Setup guides for all 13 MCP clients