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 buildto compile - Test your server with
pnpm start - Use the installation scripts to update your MCP client configuration
Next Steps
- Update
package.jsonwith 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