symbolica-mcp

A scientific computing Model Context Protocol (MCP) server allows AI, such as Claude, to perform symbolic computing, conduct calculations, analyze data, and generate visualizations. This is particularly useful for scientific and engineering applications, including quantum computing, all within a containerized environment.

Features

• Run scientific computing operations with NumPy, SciPy, SymPy, Pandas
• Perform symbolic mathematics and solve differential equations
• Support for linear algebra operations and matrix manipulations
• Quantum computing analysis
• Create data visualizations with Matplotlib and Seaborn
• Perform machine learning operations with scikit-learn
• Execute tensor operations and complex matrix calculations
• Analyze data sets with statistical tools
• Cross-platform support (automatically detects Windows, macOS, and Linux), especially for users with Mac M series chips
• Works on both Intel/AMD (x86_64) and ARM processors

Quick Start

Using the Docker image

# Pull the image from Docker Hub
docker pull ychen94/computing-mcp:latest

# Run the container (automatically detects host OS)
docker run -i --rm -v /tmp:/app/shared ychen94/computing-mcp:latest

For Windows users:

docker run -i --rm -v $env:TEMP:/app/shared ychen94/computing-mcp:latest

Integrating with Claude for Desktop

1. Open Claude for Desktop
2. Open Settings ➝ Developer ➝ Edit Config
3. Add the following configuration:

For MacOS/Linux:

{
  "mcpServers": {
    "computing-mcp": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-v",
        "/tmp:/app/shared",
        "ychen94/computing-mcp:latest"
      ]
    }
  }
}

For Windows:

{
  "mcpServers": {
    "computing-mcp": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-v",
        "%TEMP%:/app/shared",
        "ychen94/computing-mcp:latest"
      ]
    }
  }
}

Examples

Tensor Products

Can you calculate and visualize the tensor product of two matrices? Please run:

import numpy as np
import matplotlib.pyplot as plt

# Define two matrices
A = np.array([[1, 2], 
              [3, 4]])
B = np.array([[5, 6],
              [7, 8]])

# Calculate tensor product using np.kron (Kronecker product)
tensor_product = np.kron(A, B)

# Display the result
print("Matrix A:")
print(A)
print("\nMatrix B:")
print(B)
print("\nTensor Product A ⊗ B:")
print(tensor_product)

# Create a visualization of the tensor product
plt.figure(figsize=(8, 6))
plt.imshow(tensor_product, cmap='viridis')
plt.colorbar(label='Value')
plt.title('Visualization of Tensor Product A ⊗ B')

Symbolic Mathematics

Can you solve this differential equation? Please run:
import sympy as sp
import matplotlib.pyplot as plt
import numpy as np

# Define symbolic variable
x = sp.Symbol('x')
y = sp.Function('y')(x)