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Fermat MCP
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Fermat MCP

Fermat MCP is a FastMCP server that provides mathematical calculation services, supporting numerical calculations, symbolic calculations, and plotting functions. It integrates libraries such as Matplotlib, NumPy, and SymPy.
Developer toolsEducation and learning tools#Mathematical calculation#Plotting tool#Symbolic calculation.Python
2.5 points
7.4K
Open Site

Overview

Installation

Tools List

Content Details

Alternatives

What is Fermat MCP?

Fermat MCP is a FastMCP server dedicated to mathematical calculations, including numerical calculations, symbolic calculations, and data visualization. It integrates tools such as Matplotlib, NumPy, and SymPy, providing users with powerful mathematical processing capabilities.

How to use Fermat MCP?

By configuring the MCP server, you can easily use Fermat MCP for mathematical calculations in Visual Studio Code, Windsurf, or Gemini CLI. Simply set up the server according to the installation instructions and start performing various mathematical tasks.

Applicable scenarios

Fermat MCP is suitable for scenarios that require complex mathematical calculations, data analysis, and graph plotting, such as academic research, engineering analysis, and data science projects.

Main features

Numerical calculation
Supports basic arithmetic operations, statistical analysis, linear algebra, and array operations to meet daily numerical calculation needs.
Symbolic calculation
Utilizes the SymPy library for symbolic calculations such as algebraic operations, calculus, and equation solving to improve the accuracy of mathematical problems.
Data visualization
Integrates Matplotlib and supports the creation of various chart types (such as line charts, scatter plots, bar charts, etc.) to visually display data relationships.
Cross - platform support
Can be used in various development environments such as Visual Studio Code, Windsurf, and Gemini CLI, facilitating collaboration among different user groups.
Advantages
Provides comprehensive mathematical calculation functions, covering numerical and symbolic calculations
Easy to integrate into mainstream development environments, improving work efficiency
Supports a rich set of data visualization options to help understand complex data
Limitations
Requires a certain technical background to configure and use the server
May require high system resources for very complex calculations
Some advanced functions may require an additional learning curve

How to use

Install dependencies
Ensure that Python 3.12 or a higher version is installed, and install the uv package to manage project dependencies.
Clone the repository
Clone the Fermat MCP project from GitHub to your local computer.
Configure the server
Add the path of the Fermat MCP server to the configuration file according to the development environment you are using (such as Visual Studio Code or Gemini CLI).
Start the server
Run the setup.sh script to start the Fermat MCP server and get ready for mathematical calculations.

Usage examples

Calculate matrix eigenvalues
Use the NumPy module of Fermat MCP to calculate the eigenvalues of a given 8x8 matrix.
Plot function images
Use the Matplotlib function of Fermat MCP in Visual Studio Code to plot the images of the functions x² and sin(x).

Frequently Asked Questions

What dependencies does Fermat MCP require?
How to configure Fermat MCP in Visual Studio Code?
What mathematical calculations does Fermat MCP support?

Related resources

GitHub repository
Source code and documentation for the Fermat MCP project
Smithery installation guide
Detailed steps to install Fermat MCP via Smithery
Gemini CLI tutorial
Guide to installing and using Gemini CLI

Installation

Copy the following command to your Client for configuration
{
  "mcpServers": {
    "fmcp": {
      "command": "bash",
      "args": ["MCP_SERVER_ABSOLUTE_PATH/setup.sh"],
      "description": "fmcp server is for mathematical computations, including numerical and symbolic calculations, as well as plotting."
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.

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