MCP Server Boilerplate

A starter template for building MCP (Model Context Protocol) servers. This boilerplate provides a clean foundation for creating your own MCP server that can integrate with Claude, Cursor, or other MCP-compatible AI assistants.

Purpose

This boilerplate helps you quickly start building:

Custom tools for AI assistants
Resource providers for dynamic content
Prompt templates for common operations
Integration points for external APIs and services

Features

Simple "hello-world" tool example
TypeScript support with proper type definitions
Easy installation scripts for different MCP clients
Clean project structure ready for customization

How It Works

This MCP server template provides:

A basic server setup using the MCP SDK
Example tool implementation
Build and installation scripts
TypeScript configuration for development

The included example demonstrates how to create a simple tool that takes a name parameter and returns a greeting.

Getting Started

# Clone the boilerplate git clone <your-repo-url> cd mcp-server-boilerplate # Install dependencies pnpm install # Build the project pnpm run build # Start the server pnpm start

Installation Scripts

This boilerplate includes convenient installation scripts for different MCP clients:

# For Claude Desktop pnpm run install-desktop # For Cursor pnpm run install-cursor # For Claude Code pnpm run install-code # Generic installation pnpm run install-server

These scripts will build the project and automatically update the appropriate configuration files.

Usage with Claude Desktop

The installation script will automatically add the configuration, but you can also manually add it to your claude_desktop_config.json file:

{ "mcpServers": { "your-server-name": { "command": "node", "args": ["/path/to/your/dist/index.js"] } } }

Then restart Claude Desktop to connect to the server.

Customizing Your Server

Adding Tools

Tools are functions that the AI assistant can call. Here's the basic structure:

server.tool( "tool-name", "Description of what the tool does", { // Zod schema for parameters param1: z.string().describe("Description of parameter"), param2: z.number().optional().describe("Optional parameter"), }, async ({ param1, param2 }) => { // Your tool logic here return { content: [ { type: "text", text: "Your response", }, ], }; } );

Adding Resources

Resources provide dynamic content that the AI can access:

server.resource( "resource://example/{id}", "Description of the resource", async (uri) => { // Extract parameters from URI const id = uri.path.split("/").pop(); return { contents: [ { uri, mimeType: "text/plain", text: `Content for ${id}`, }, ], }; } );

Adding Prompts

Prompts are reusable templates:

server.prompt( "prompt-name", "Description of the prompt", { // Parameters for the prompt topic: z.string().describe("The topic to discuss"), }, async ({ topic }) => { return { description: `A prompt about ${topic}`, messages: [ { role: "user", content: { type: "text", text: `Please help me with ${topic}`, }, }, ], }; } );

Project Structure

├── src/ │ └── index.ts # Main server implementation ├── scripts/ # Installation and utility scripts ├── dist/ # Compiled JavaScript (generated) ├── package.json # Project configuration ├── tsconfig.json # TypeScript configuration └── README.md # This file

Development

Make changes to src/index.ts
Run pnpm run build to compile
Test your server with pnpm start
Use the installation scripts to update your MCP client configuration

Next Steps

Update package.json with your project details
Customize the server name and tools in src/index.ts
Add your own tools, resources, and prompts
Integrate with external APIs or databases as needed

License

MIT

JSON MCP Boilerplate