MCP Learning
A learning-focused MCP server that demonstrates how to build arithmetic tools for AI assistants, currently featuring addition functionality with structured input/output.
README
MCP Learning
A Model Context Protocol (MCP) server that provides arithmetic tools for AI assistants like Claude.
Features
- Add Tool: Add two numbers together with structured input/output
Installation
Prerequisites
- Node.js 18 or higher
- npm, yarn, or pnpm
From Git Repository
# Clone the repository
git clone https://github.com/sadjad-chrono/mcp-learning.git
cd mcp-learning
# Install dependencies
pnpm install
# or
npm install
# Build the project
pnpm build
# or
npm run build
Usage with Claude Desktop
To use this MCP server with Claude Desktop, you need to add it to your Claude configuration file.
Configuration Steps
-
Locate your Claude Desktop config file:
- macOS:
~/Library/Application Support/Claude/claude_desktop_config.json - Windows:
%APPDATA%\Claude\claude_desktop_config.json - Linux:
~/.config/Claude/claude_desktop_config.json
- macOS:
-
Add the server configuration:
Option 1: Using npx (recommended after publishing to npm)
{
"mcpServers": {
"mcp-learning": {
"command": "npx",
"args": [
"-y",
"@sadjadteh-chrono/mcp-learning"
]
}
}
}
Option 2: Using the built package from cloned repo
After building the project, add this to your claude_desktop_config.json:
{
"mcpServers": {
"mcp-learning": {
"command": "node",
"args": [
"/absolute/path/to/mcp-learning/dist/mcpserver/index.js"
]
}
}
}
Option 3: Development mode with tsx
{
"mcpServers": {
"mcp-learning": {
"command": "npx",
"args": [
"-y",
"tsx",
"/absolute/path/to/mcp-learning/src/mcpserver/index.ts"
]
}
}
}
- Restart Claude Desktop to load the new configuration.
Verifying the Installation
Once configured and Claude Desktop is restarted:
- Open a new conversation in Claude
- Look for the 🔌 icon or hammer icon indicating MCP tools are available
- Try using the add tool by asking Claude to "add 5 and 3"
Development
Project Structure
mcp-learning/
├── src/
│ └── mcpserver/
│ └── index.ts # Main MCP server implementation
├── dist/ # Compiled JavaScript (generated)
├── package.json
├── tsconfig.json
└── README.md
Available Scripts
# Build the project
pnpm build
# Run in development mode
pnpm dev
# Run in development mode with auto-reload
pnpm dev:watch
# Run with debugger
pnpm debug
# Clean build artifacts
pnpm clean
Testing with MCP Inspector
You can test the server using the MCP Inspector:
npx @modelcontextprotocol/inspector tsx src/mcpserver/index.ts
This will open a web interface where you can interact with your MCP server and test tools.
Adding New Tools
To add new tools to your MCP server, edit src/mcpserver/index.ts:
server.registerTool(
"tool-name",
{
title: "Tool Title",
description: "What the tool does",
inputSchema: {
param1: z.string().describe("Description of param1"),
// Add more parameters
},
outputSchema: { result: z.string() },
},
async ({ param1 }) => {
// Tool implementation
return {
content: [{ type: "text", text: "result" }],
structuredContent: { result: "result" },
};
}
);
Publishing to Git
# Initialize git repository (if not already done)
git init
# Add all files
git add .
# Create initial commit
git commit -m "Initial commit: MCP learning server"
# Add remote repository
git remote add origin https://github.com/sadjad-chrono/mcp-learning.git
# Push to GitHub
git push -u origin main
Publishing to npm
Prerequisites
- Create an npm account at https://www.npmjs.com/signup
- Login to npm:
npm login
Publish Steps
# Build the package
pnpm build
# Publish to npm (scoped packages are public by default for free accounts)
npm publish --access public
Note: The --access public flag is required for scoped packages on free npm accounts.
Then users can install with:
npm install -g @sadjadteh-chrono/mcp-learning
# or use with npx
npx @sadjadteh-chrono/mcp-learning
License
MIT
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。