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Computing MCP

Symbolica - mcp is a scientific computing model context protocol (MCP) server that provides functions such as symbolic computation, data analysis, visualization, and quantum computing, and supports running in a containerized environment.
Developer toolsResearch and data#Scientific Computing#Symbolic Mathematics#Quantum Analysis#Data Visualization
2.5 points
7.5K
Open Site

Overview

Installation

Content Details

Alternatives

What is the Scientific Computing MCP Server?

The scientific computing MCP server is a containerized scientific computing platform that supports multiple programming languages and tools for performing tasks such as mathematical operations, data analysis, machine learning, and quantum computing.

How to Use the Scientific Computing MCP Server?

Users can run the server through Docker containers and integrate it into AI tools via configuration files. The server supports multiple programming languages and libraries, such as NumPy, SymPy, and Matplotlib.

Applicable Scenarios

Suitable for researchers, engineers, and data scientists to conduct complex data analysis, modeling, and visualization.

Main Features

Numerical Computation
Supports efficient numerical computation using NumPy and SciPy.
Symbolic Computation
Performs algebraic operations and symbolic expression processing using SymPy.
Data Visualization
Generates high - quality charts using Matplotlib and Seaborn.
Machine Learning
Supports machine learning model training using scikit - learn.
Quantum Computing
Performs quantum state simulation and quantum algorithm implementation.
Advantages
Cross - platform support (Windows, macOS, Linux)
High - performance computing capabilities
Rich plugin ecosystem
Limitations
Requires Docker environment installation
Has certain requirements for hardware performance

How to Use

Pull the Docker Image
Run the following command in the terminal to download the latest version of the Docker image: ```bash docker pull ychen94/computing - mcp:latest ```
Start the Container
Run the following command to start the MCP server container: ```bash docker run -i --rm -v /tmp:/app/shared ychen94/computing - mcp:latest ```
Configure AI Tools
Edit the configuration file of the AI tool and add relevant settings for the MCP server.

Usage Examples

Calculate the Tensor Product of Matrices
Demonstrate how to use NumPy to calculate the tensor product of two matrices and generate a visualization result.
Solve Differential Equations
Show how to use SymPy to solve differential equations and plot their solutions.

Frequently Asked Questions

How to Solve Permission Errors?
Why Can't the Generated Image Be Displayed?

Related Resources

Docker Official Documentation
Learn about the basic operations and best practices of Docker.
GitHub Project Repository
Get more information and source code about the MCP server.
Symbolic Computing Tutorial
Learn how to use SymPy for symbolic computation.

Installation

Copy the following command to your Client for configuration
{
  "mcpServers": {
    "computing-mcp": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-v",
        "/tmp:/app/shared",
        "ychen94/computing-mcp:latest"
      ]
    }
  }
}

{
  "mcpServers": {
    "computing-mcp": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-v",
        "%TEMP%:/app/shared",
        "ychen94/computing-mcp:latest"
      ]
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.

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