Fermatmcp

by abhiphile

227 downloads
Not rated
GitHub

About

This project provides a FastMCP server for mathematical computations, including numerical and symbolic calculations, as well as plotting.

Details

Author
abhiphile
Downloads
227
Categories
Other, Knowledge Base

- Supports numerical calculations (e.g., gcd, lcm, sqrt, power)
- Supports symbolic mathematical calculations
- Supports plotting of mathematical functions
- Works with VS Code, Windsurf, and Gemini CLI
- Quick setup using a single bash script

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

  1. Download and install Highlight from highlightai.com/download
  2. Navigate to the plugins tab and select "Add Custom Plugin"
  3. Configure the plugin with the settings below
    Plugin Name Fermatmcp
    Command (node, npx, python, etc.)

    Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.

  4. Enable "Start Automatically" if you want the plugin to start when Highlight launches

From the repository

Clone the repository from GitHub, ensure Python 3.12 or higher and uv are installed, then configure the MCP client by adding a server entry in its mcp.json file that runs the provided setup.sh script. Detailed instructions are available for VS Code, Windsurf, and Gemini CLI.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "fermatmcp": {
            "fmcp": {
                "command": "bash",
                "args": [
                    "ABSOLUTE_PATH_TO_CLONED_FERMAT_MCP/setup.sh"
                ],
                "description": "fmcp server is for mathematical computations, including numerical and symbolic calculations, as well as plotting."
            }
        }
    }
}

McpServers

{
    "fmcp": {
        "command": "bash",
        "args": [
            "ABSOLUTE_PATH_TO_CLONED_FERMAT_MCP/setup.sh"
        ],
        "description": "fmcp server is for mathematical computations, including numerical and symbolic calculations, as well as plotting."
    }
}

This project provides a FastMCP server for mathematical computations, including numerical and symbolic calculations, as well as plotting.

-

Python 3.12 or higher (To install Python3.12 followPython Download)

uv (To install uv followuv Installation)

git clone https://github.com/abhiphile/fermat-mcp

You can find themcp.jsonfile in the MCP: Open User Configuration or MCP: Open Workspace 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." } } }

If you're using Claude or the Anthropic MCP client, add this working MCP configuration to yourmcp.json(update the directory path to your local clone):

{ "mcpServers": { "fmcp": { "command": "uv", "args": [ "--directory", "/home/ty/Repositories/fermat-mcp", "run", "server.py" ] } } }

-

Open your Gemini settings JSON located in ~/.gemini/settings.json where ~ is your home directory.

Add the following to your settings.json:

{ "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." } } }

To install Fermat MCP for local usage automatically viaSmithery:

npx -y @smithery/cli install @abhiphile/fermat-mcp --client gemini
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ > Can you use fmcp server and using numpy method find the eigen values of this 88 matrix, | │ 2 1 3 1 1 8 4 2 | │ 6 6 0 7 1 4 6 1 │ │ 9 2 1 8 7 9 9 0 │ │ 2 5 6 6 9 8 0 1 │ │ 1 3 6 2 3 8 8 1 │ │ 9 4 2 2 1 2 2 9 │ │ 8 6 4 4 2 0 2 8 │ │ 0 0 0 6 6 7 5 6 │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ ╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ ✔ numpy_mcp_numerical_operation (fmcp MCP Server) {"a":[[2,1,3,1,1,8,4,2],[6,6,0,7,1,4,6,1],[9,2,1,8,7,9,9,0],[2,5,6,6,9,8,0,1],[1,3,… │ │ │ │ {"eigenvalues":["32.077244457548815+0j","-11.531090644775198+0j","-6.6653982146786195+0j","0.6715984762411508+3.37024850 │ │ 10270413j","0.6715984762411508-3.3702485010270413j","4.541270555490195+2.776364664923869j","4.541270555490195-2.77636466 │ │ 4923869j","3.6935063384423428+0j"],"eigenvectors":[["-0.23263835483680192+0j","-0.2264723575289234+0j","-0.4308391916391 │ │ 0195+0j","-0.012346573390129022+0.17748655663058255j","-0.012346573390129022-0.17748655663058255j","-0.21221572277027187 │ │ +0.3524396218277479j","-0.21221572277027187-0.3524396218277479j","0.3451499664861578+0j"],["-0.31955742545335186+0j","-0 │ │ .2569860493445581+0j","0.05691886770041556+0j","-0.35591013681869693-0.2242364092694275j","-0.35591013681869693+0.224236 │ │ 4092694275j","0.1932161673963751-0.39527849111641133j","0.1932161673963751+0.39527849111641133j","-0.7979681696063214+0j │ │ "],["-0.46626263247473404+0j","-0.4684914620112376+0j","0.5469400556350749+0j","0.34325164099973565+0.06607019711949293j │ │ ","0.34325164099973565-0.06607019711949293j","0.21312270185159682+0.28822307710358636j","0.21312270185159682-0.288223077 │ │ 10358636j","0.42707422750984786+0j"],["-0.41589316441674523+0j","0.2291771012892302+0j","0.09410792992600435+0j","0.6375 │ │ 92441360358+0j","0.637592441360358+-0j","0.46446646137729414+0j","0.46446646137729414+-0j","0.08171661775583623+0j"],["- │ │ 0.35812884189789035+0j","-0.26551071423139044+0j","-0.649979374400915+0j","-0.2999153430497845+0.20110182336747695j","-0 │ │ .2999153430497845-0.20110182336747695j","0.04424367733961273-0.020400357434930304j","0.04424367733961273+0.0204003574349 │ │ 30304j","-0.037276545325188024+0j"],["-0.3042416233100104+0j","0.4027405071320744+0j","0.2343447346668298+0j","-0.119224 │ │ 87219398595+0.10317726668781271j","-0.11922487219398595-0.10317726668781271j","-0.3387759141362585+0.15790241128043445j" │ │ ,"-0.3387759141362585-0.15790241128043445j","0.11473144194899779+0j"],["-0.35389732755448017+0j","0.5359362131480812+0j" │ │ ,"0.13989902586074168+0j","-0.16008513226461316-0.26111873108374495j","-0.16008513226461316+0.26111873108374495j","0.055 │ │ 729749650881726-0.22748184206919145j","0.055729749650881726+0.22748184206919145j","-0.19838514696566653+0j"],["-0.327615 │ │ 5213691367+0j","-0.30122843153433887+0j","0.07858579556736099+0j","0.00042251379361638556-0.11670182367986544j","0.00042 │ │ 251379361638556+0.11670182367986544j","-0.18590228871569267-0.2479130526139182j","-0.18590228871569267+0.247913052613918 │ │ 2j","-0.03374593856285834+0j"]]} │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ ✦ Of course, I can do that. Here are the eigenvalues for the given matrix: 32.077244457548815 -11.531090644775198 -6.6653982146786195 0.6715984762411508 + 3.3702485010270413j 0.6715984762411508 - 3.3702485010270413j 4.541270555490195 + 2.776364664923869j 4.541270555490195 - 2.776364664923869j 3.6935063384423428

- Using Visual Studio Code, Windsurf Here the task was to plot both x*2 and sin(x) in the same plot.

Search 3,500+ US universities, estimate admission chances, check financial aid by income bracket, and build balanced college lists. Free MCP server, no API key required. Data from IPEDS and College Scorecard

Calculate permeability of porous media using the seepage distance method, derived from the Darcy's law-based formula

A high-performance Model Context Protocol (MCP) server for math-ing whilst vibing with LLMs. Built with Polars, Pandas, NumPy, SciPy, and SymPy for optimal calculation speed and comprehensive mathematical capabilities from basic arithmetic to advanced calculus and linear algebra.

Provides astronomical data such as celestial object positions, rise/set times, and visibility information.

A collection of 19 professional tools for chemical molecular processing based on the Model Context Protocol (MCP).

An MCP server for Hierarchical Event Descriptors (HED) that automates sidecar creation and annotation for BIDS event files using LLMs.

Educational MCP server with math operations, statistics, visualizations, and persistent workspace.

An intelligent tutoring server that uses GitHub documentation repositories to provide structured educational prompts and tools.

Provides seamless access to particle physics data from the Particle Data Group (PDG) for AI assistants and applications.

No reviews yet — be the first

Sign in to leave a review

Use Google, GitHub, or an email account so ratings stay tied to real people.

Email sign in

No reviews posted yet.