项目介绍
Fermat MCP
该项目提供了一个用于数学计算的FastMCP服务器，包括数值和符号计算以及绘图。
模块
1. mpl_mcp - Matplotlib 集成
| 功能 | 描述 |
|---|---|
plot_barchart | 绘制给定数据值的条形图 |
plot_scatter | 根据数据点创建散点图 |
plot_chart | 绘制线图、散点图或条形图 |
plot_stem | 创建离散数据的茎叶图 |
plot_stack | 生成堆叠区域图或条形图 |
eqn_chart | 绘制数学方程 |
2. numpy_mcp - NumPy 集成
| 类别 | 操作 |
|---|---|
| 基础数学 | 加法、减法、乘法、除法、幂、绝对值、指数、对数、平方根 |
| 三角函数 | 正弦、余弦、正切 |
| 统计学 | 均值、中位数、标准差、方差、最小值、最大值、argmin、argmax、百分位数 |
| 线性代数 | 点积、矩阵乘法、逆矩阵、行列式、特征值、求解线性方程组、奇异值分解 |
| 矩阵操作 | 创建、零矩阵、单位矩阵、全填充矩阵、arange、linspace |
| 数组操作 | 重塑、展平、连接、转置、堆叠 |
3. sympy_mcp - SymPy 集成
| 类别 | 操作 |
|---|---|
| 代数 | 化简、展开、因式分解、收集项 |
| 微积分 | 求导、积分、极限、级数 |
| 方程 | 解方程、解集、线性方程组求解、非线性方程组求解 |
| 矩阵操作 | 创建、行列式、逆矩阵、简化阶梯形式、特征值 |
安装
要求
克隆仓库
git clone https://github.com/abhiphile/fermat-mcp
Visual Studio Code, Windsurf
您可以在
MCP: 打开用户配置或 MCP: 打开工作区配置中找到 mcp.json 文件。
向您的 mcp.json 添加以下内容：
{
"mcpServers": {
"fmcp": {
"command": "bash",
"args": ["MCP_SERVER_ABSOLUTE_PATH/setup.sh"],
"description": "fmcp 服务器用于数学计算，包括数值和符号计算以及绘图。"
}
}
}
Claude (Anthropic)
如果您使用的是Claude或Anthropic MCP客户端，请向您的 mcp.json 添加以下工作配置（更新到本地克隆的目录）：
{
"mcpServers": {
"fmcp": {
"command": "uv",
"args": [
"--directory",
"/home/ty/Repositories/fermat-mcp",
"run",
"server.py"
]
}
}
}
Gemini CLI
-
打开位于~/.gemini/settings.json的Gemini设置JSON文件，其中~是您的主目录。
-
向您的settings.json添加以下内容：
{
"mcpServers": {
"fmcp": {
"command": "bash",
"args": ["MCP_SERVER_ABSOLUTE_PATH/setup.sh"],
"description": "fmcp 服务器用于数学计算，包括数值和符号计算以及绘图。"
}
}
}
通过Smithery安装
要通过Smithery自动安装Fermat MCP以供本地使用：
npx -y @smithery/cli install @abhiphile/fermat-mcp --client gemini
示例用法
- 使用Gemini CLI
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ > 您能否使用fmcp服务器并通过numpy方法找到这个8*8矩阵的特征值？ |
│ 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"]]} │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ 当然可以。以下是给定矩阵的特征值：
32.077244457548815
-11.531090644775198
-6.6653982146786195
0.6715984762411508 + 3.3702485010270413j
0.6715984762411508 - 3.3702485010270413j
4.541270555490195 + 2.776364664923869j
4.541270555490195 - 2.776364664923869j
3.693