Fermat MCP
by abhiphile
README.md
# Fermat MCP
[](https://smithery.ai/server/@abhiphile/fermat-mcp)
[](https://mseep.ai/app/16469d0f-0c4a-4b35-babf-4666107251f5)
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 |
## Setup
### Requirements
- Python 3.12 or higher (To install Python3.12 follow [Python Download](https://www.python.org/downloads/))
- uv (To install uv follow [uv Installation](https://docs.astral.sh/uv/getting-started/installation/))
#### Clone the repository
```bash
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`:
```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):
```json
{
"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:
```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](https://smithery.ai/server/@abhiphile/fermat-mcp):
```bash
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.
<a href="https://glama.ai/mcp/servers/@abhiphile/fermat-mcp">
<img width="380" height="200" src="https://glama.ai/mcp/servers/@abhiphile/fermat-mcp/badge" />
</a>
TDQS
C2.6/5.0
Scored 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
ActivityInactive
ResponsivenessSlow