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MCP Server

Fermat MCP

A FastMCP server for mathematical computations, including numerical and symbolic calculations with NumPy and SymPy integration, as well as data visualization through Matplotlib.

5
GitHub Stars
8/23/2025
Last Updated
MCP Server Configuration
1{
2 "name": "fmcp",
3 "command": "bash",
4 "args": [
5 "MCP_SERVER_ABSOLUTE_PATH/setup.sh"
6 ],
7 "description": "fmcp server is for mathematical computations, including numerical and symbolic calculations, as well as plotting."
8}
JSON8 lines

README Documentation

Fermat MCP

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

Modules

1. mpl_mcp - Matplotlib Integration

FeatureDescription
plot_barchartPlots bar charts of given data values
plot_scatterCreates scatter plots from data points
plot_chartPlots line, scatter, or bar charts
plot_stemCreates stem plots for discrete data
plot_stackGenerates stacked area/bar charts
eqn_chartPlots mathematical equations

2. numpy_mcp - NumPy Integration

CategoryOperations
Basic Mathadd, sub, mul, div, power, abs, exp, log, sqrt
Trigonometricsin, cos, tan
Statisticsmean, median, std, var, min, max, argmin, argmax, percentile
Linear Algebradot, matmul, inv, det, eig, solve, svd
Matrix Operationscreate, zeros, ones, full, arange, linspace
Array Manipulationreshape, flatten, concatenate, transpose, stack

3. sympy_mcp - SymPy Integration

CategoryOperations
Algebrasimplify, expand, factor, collect
Calculusdiff, integrate, limit, series
Equationssolve, solveset, linsolve, nonlinsolve
Matrix Operationscreate, det, inv, rref, eigenvals

Setup

Requirements

Clone the repository

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

Visual Studio Code, Windsurf

You can find the mcp.json file in the MCP: Open User Configuration or MCP: Open Workspace Configuration

vs-code-1

Add the following to your mcp.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."
    }
  }
}

Gemini CLI

  • 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."
    }
  }
}

Installing via Smithery

To install Fermat MCP for local usage automatically via Smithery:

npx -y @smithery/cli install @abhiphile/fermat-mcp --client gemini

Example Usage

  • Using Gemini CLI
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│  > Can you use fmcp server and using numpy method find the eigen values of this 8*8 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. GUI Demo 1-2 GUI Demo 2-2

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