What can you do with this server?

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

Which integrations are available for this server?

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.

How do I use Fermat MCP?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Fermat MCP plot a scatter chart for x=[1,2,3,4,5] and y=[2,4,9,16,25]" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Fermat MCP

by abhiphile

Overview Schema Source Related Servers Discussions Admin

Python

Local

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
`plot_barchart`	Plots bar charts of given data values
`plot_scatter`	Creates scatter plots from data points
`plot_chart`	Plots line, scatter, or bar charts
`plot_stem`	Creates stem plots for discrete data
`plot_stack`	Generates stacked area/bar charts
`eqn_chart`	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

Related MCP server: mcp-sympy

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

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."
    }
  }
}

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.

Available Tools

12 tools

mpl_mcp_eqn_chartD

Plots mathematical equations

Inputs: equations (required), x_min, x_max, num_points, +11 more.

mpl_mcp_plot_barchartD

Plots barchart of given datavalues

Inputs: values (required), labels, title, xlabel, +5 more.

mpl_mcp_plot_chartC

Plots line/scatter/bar chart of given datavalues

Inputs: x_data (required), y_data (required), plot_type, labels, +9 more.

mpl_mcp_plot_scatterC

Plots scatter chart of given datavalues

Inputs: x_data (required), y_data (required), labels, title, +13 more.

mpl_mcp_plot_stackC

Plots stacked area/bar chart of given datavalues

Inputs: x_data (required), y_data (required), chart_type, labels, +9 more.

mpl_mcp_plot_stemD

Plots stem chart of given datavalues

Inputs: x_data (required), y_data (required), labels, title, +12 more.

numpy_mcp_matlib_operationC

Do matrix operations: rand-mat, zeros, ones, eye, identity, arange, linspace, reshape, flatten, concatenate, transpose, stack

Inputs: operation (required), data, shape, m, +7 more. Returns: result

numpy_mcp_numerical_operationD

Do numerical operation like add, sub, mul, div, power, abs, exp, log, sqrt, sin, cos, tan, mean, median, std, var, min, max, argmin, argmax, percentile, dot, matmul, inv, det, eig, solve, svd

Inputs: operation (required), a, b, shape, +8 more. Returns: result

sympy_mcp_algebra_operationC

Do algebraic operations like simplify, expand, factor, collect

Inputs: operation (required), expr (required), syms, rational, +14 more. Returns: result

sympy_mcp_calculus_operationC

Do calculus operations like diff, integrate, limit, series

Inputs: operation (required), expr (required), sym, n, +5 more. Returns: result

sympy_mcp_equation_operationC

Do symbolic equation operations like solve, solveset, linsolve, nonlinsolve

Inputs: operation (required), equations (required), symbols, domain, +6 more. Returns: result

sympy_mcp_matrix_operationC

Do symbolic matrix operations like create, det, inv, rref, eigenvals

Inputs: operation (required), data (required), rational, nrows, +2 more.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

Deploy Server

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TDQS

C2.6/5.0

Scored 2026-04-18 09:15 across 12 tools

Disambiguation3/5

The tools are grouped by library (mpl, numpy, sympy) with clear distinctions between groups, but within groups there is significant overlap. For example, mpl_mcp_plot_chart can plot line/scatter/bar charts, making mpl_mcp_plot_barchart and mpl_mcp_plot_scatter partially redundant. Similarly, numpy_mcp_numerical_operation includes matrix operations that overlap with numpy_mcp_matlib_operation, and sympy tools have some functional overlap (e.g., equation solving appears in multiple places). Descriptions help, but agents may struggle to choose between overlapping tools.

Naming Consistency4/5

Naming follows a consistent pattern of library_prefix_mcp_domain_operation (e.g., mpl_mcp_plot_chart, numpy_mcp_matlib_operation). All tools use snake_case consistently. Minor deviations exist, such as 'matlib' vs 'matrix' in numpy tools and slight variations in domain terms (e.g., 'algebra_operation' vs 'calculus_operation'), but the overall structure is predictable and readable.

Tool Count4/5

With 12 tools, the count is reasonable for a mathematical/plotting server covering multiple libraries (matplotlib, numpy, sympy). It's slightly on the higher side but justified by the broad scope. Each tool groups related functionalities, though some consolidation might reduce overlap. The count aligns well with the server's purpose of providing mathematical operations and visualizations.

Completeness4/5

The server covers key mathematical domains: plotting (multiple chart types), numerical operations (basic and advanced), symbolic algebra, calculus, and matrix operations. There are minor gaps, such as no explicit tool for statistical distributions beyond basic stats in numpy, and plotting tools might lack 3D or specialized visualizations. However, core workflows for mathematical analysis and visualization are well-supported, with no major dead ends.

Maintenance

ActivityMaintained

ResponsivenessOutdated data

Resources

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Fermat MCP

Modules

1. mpl_mcp - Matplotlib Integration

2. numpy_mcp - NumPy Integration

3. sympy_mcp - SymPy Integration

Setup

Requirements

Clone the repository

Visual Studio Code, Windsurf

Claude (Anthropic)

Gemini CLI

Installing via Smithery

Example Usage

Available Tools

Tool Schema Changelog

TDQS

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

NumPy Calculator

mcp-sympy

Data Processing MCP Server

Math MCP Server

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