-
securityA
license-
qualityA server that provides access to Mathematica documentation through FastMCP, enabling users to retrieve function documentation and list package symbols from Wolfram Mathematica.
Last updated -
8
MIT License
Fermat MCP is a FastMCP server for comprehensive mathematical computations including numerical calculations, symbolic algebra, and data visualization.
Matplotlib Integration (Visualization)
Create various chart types: bar charts (vertical/horizontal), scatter plots, line charts, stem plots, stacked area/bar charts, and mathematical equation plots
Customize plots with DPI, figure sizes, titles, labels, colors, gridlines, legends, markers, alpha transparency, and save options
Support for multiple datasets on single plots with flexible data input formats
NumPy Integration (Numerical Operations)
Basic Math: Addition, subtraction, multiplication, division, power, absolute value, exponential, logarithm, square root
Trigonometry: Sine, cosine, tangent functions
Statistics: Mean, median, standard deviation, variance, min/max, argmin/argmax, percentiles
Linear Algebra: Dot product, matrix multiplication, inverse, determinant, eigenvalues/eigenvectors, solve linear systems, SVD decomposition
Array Manipulation: Create matrices (zeros, ones, identity, arange, linspace), reshape, flatten, concatenate, transpose, stack
SymPy Integration (Symbolic Mathematics)
Algebra: Simplify, expand, factor, and collect algebraic expressions
Calculus: Differentiation, integration (definite/indefinite), limits, Taylor series expansion
Equation Solving: Solve algebraic equations, solve sets, linear systems (linsolve), and nonlinear systems (nonlinsolve)
Symbolic Matrices: Create symbolic matrices, compute determinants, inverses, reduced row echelon form (RREF), eigenvalues
Key Features
Multi-client support: Works with Claude, Gemini CLI, Visual Studio Code, and Windsurf
Easy setup via Smithery installation or manual configuration
Production-ready with comprehensive documentation (verified on MseeP platform)
Flexible output: Returns numerical results, symbolic expressions, and generates visual plots
Enables numerical computations with NumPy including basic math operations, trigonometric functions, statistical calculations, linear algebra operations, matrix operations, and array manipulation.
Provides symbolic mathematics capabilities including algebraic operations, calculus functions, equation solving, and matrix operations through SymPy integration.
Fermat MCP
This project provides a FastMCP server for mathematical computations, including numerical and symbolic calculations, as well as plotting.
Modules
1. mpl_mcp - Matplotlib Integration
Feature | Description |
| Plots bar charts of given data values |
| Creates scatter plots from data points |
| Plots line, scatter, or bar charts |
| Creates stem plots for discrete data |
| Generates stacked area/bar charts |
| Plots mathematical equations |
2. numpy_mcp - NumPy Integration
Category | Operations |
Basic Math | add, sub, mul, div, power, abs, exp, log, sqrt |
Trigonometric | sin, cos, tan |
Statistics | mean, median, std, var, min, max, argmin, argmax, percentile |
Linear Algebra | dot, matmul, inv, det, eig, solve, svd |
Matrix Operations | create, zeros, ones, full, arange, linspace |
Array Manipulation | reshape, flatten, concatenate, transpose, stack |
3. sympy_mcp - SymPy Integration
Category | Operations |
Algebra | simplify, expand, factor, collect |
Calculus | diff, integrate, limit, series |
Equations | solve, solveset, linsolve, nonlinsolve |
Matrix Operations | create, det, inv, rref, eigenvals |
Setup
Requirements
Python 3.12 or higher (To install Python3.12 follow Python Download)
uv (To install uv follow uv Installation)
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
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."
}
}
}
Claude (Anthropic)
If you're using Claude or the Anthropic MCP client, add this working MCP configuration to your mcp.json (update the directory path to your local clone):
{
"mcpServers": {
"fmcp": {
"command": "uv",
"args": [
"--directory",
"/home/ty/Repositories/fermat-mcp",
"run",
"server.py"
]
}
}
}
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.
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