Boilerplate MCP Server

This project serves as a foundation for developing custom Model Context Protocol (MCP) servers that connect AI assistants to external data sources or APIs. It provides a complete architecture pattern, a working example tool, and development infrastructure ready for extension.

Overview

What is MCP?

Model Context Protocol (MCP) is an open standard that allows AI systems to securely and contextually connect with external tools and data sources.

This boilerplate implements the MCP specification with a clean, layered architecture that can be extended to build custom MCP servers for any API or data source.

Why Use This Boilerplate?

• Production-Ready Architecture: Follows the same pattern used in published MCP servers, with clear separation between CLI, tools, controllers, and services.

• Type Safety: Built with TypeScript for improved developer experience, code quality, and maintainability.

• Working Example: Includes a fully implemented IP lookup tool demonstrating the complete pattern from CLI to API integration.

• Testing Framework: Comes with testing infrastructure for both unit and CLI integration tests, including coverage reporting.

• Development Tooling: Includes ESLint, Prettier, TypeScript, and other quality tools preconfigured for MCP server development.

Getting Started

Prerequisites

• Node.js (>=18.x): Download
• Git: For version control

Step 1: Clone and Install

# Clone the repository
git clone https://github.com/aashari/boilerplate-mcp-server.git
cd boilerplate-mcp-server

# Install dependencies
npm install

Step 2: Run Development Server

Start the server in development mode:

npm run dev:server

This starts the MCP server with hot-reloading and enables the MCP Inspector at http://localhost:5173.

Step 3: Test the Example Tool

Run the example IP lookup tool from the CLI:

# Using CLI in development mode
npm run dev:cli -- get-ip-details

# Or with a specific IP
npm run dev:cli -- get-ip-details 8.8.8.8

Architecture

This boilerplate follows a clean, layered architecture pattern that separates concerns and promotes maintainability.

Project Structure

src/
├── cli/              # Command-line interfaces
├── controllers/      # Business logic
├── services/         # External API interactions
├── tools/            # MCP tool definitions
├── types/            # Type definitions
├── utils/            # Shared utilities
└── index.ts          # Entry point

Layers and Responsibilities

CLI Layer (src/cli/*.cli.ts)

• Purpose: Define command-line interfaces that parse arguments and call controllers
• Naming: Files should be named <feature>.cli.ts
• Testing: CLI integration tests in <feature>.cli.test.ts

Tools Layer (src/tools/*.tool.ts)

• Purpose: Define MCP tools with schemas and descriptions for AI assistants
• Naming: Files should be named <feature>.tool.ts with types in <feature>.types.ts
• Pattern: Each tool should use zod for argument validation

Controllers Layer (src/controllers/*.controller.ts)

• Purpose: Implement business logic, handle errors, and format responses
• Naming: Files should be named <feature>.controller.ts
• Pattern: Should return standardized ControllerResponse objects

Services Layer (src/services/*.service.ts)

• Purpose: Interact with external APIs or data sources
• Naming: Files should be named <feature>.service.ts
• Pattern: Pure API interactions with minimal logic

Utils Layer (src/utils/*.util.ts)

• Purpose: Provide shared functionality across the application
• Key Utils:
  • logger.util.ts: Structured logging
  • error.util.ts: Error handling and standardization
  • formatter.util.ts: Markdown formatting helpers

Development Guide

Development Scripts

# Start server in development mode (hot-reload & inspector)
npm run dev:server

# Run CLI in development mode
npm run dev:cli -- [command] [args]

# Build the project
npm run build

# Start server in production mode
npm run start:server

# Run CLI in production mode
npm run start:cli -- [command] [args]

Testing

# Run all tests
npm test

# Run specific tests
npm test -- src/path/to/test.ts

# Generate test coverage report
npm run test:coverage

Code Quality

# Lint code
npm run lint

# Format code with Prettier
npm run format

# Check types
npm run typecheck

Building Custom Tools

Follow these steps to add your own tools to the server:

1. Define Service Layer

Create a new service in src/services/ to interact with your external API:

// src/services/example.service.ts
import { Logger } from '../utils/logger.util.js';

const logger = Logger.forContext('services/example.service.ts');

export async function getData(param: string): Promise<any> {
	logger.debug('Getting data', { param });
	// API interaction code here
	return { result: 'example data' };
}

2. Create Controller

Add a controller in src/controllers/ to handle business logic:

// src/controllers/example.controller.ts
import { Logger } from '../utils/logger.util.js';
import * as exampleService from '../services/example.service.js';
import { formatMarkdown } from '../utils/formatter.util.js';
import { handleControllerError } from '../utils/error-handler.util.js';
import { ControllerResponse } from '../types/common.types.js';

const logger = Logger.forContext('controllers/example.controller.ts');

export interface GetDataOptions {
	param?: string;
}

export async function getData(
	options: GetDataOptions = {},
): Promise<ControllerResponse> {
	try {
		logger.debug('Getting data with options', options);

		const data = await exampleService.getData(options.param || 'default');

		const content = formatMarkdown(data);

		return { content };
	} catch (error) {
		throw handleControllerError(error, {
			entityType: 'ExampleData',
			operation: 'getData',
			source: 'controllers/example.controller.ts',
		});
	}
}

3. Implement MCP Tool

Create a tool definition in src/tools/:

// src/tools/example.tool.ts
import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js';
import { z } from 'zod';
import { Logger } from '../utils/logger.util.js';
import { formatErrorForMcpTool } from '../utils/error.util.js';
import * as exampleController from '../controllers/example.controller.js';

const logger = Logger.forContext('tools/example.tool.ts');

const GetDataArgs = z.object({
	param: z.string().optional().describe('Optional parameter'),
});

type GetDataArgsType = z.infer<typeof GetDataArgs>;

async function handleGetData(args: GetDataArgsType) {
	try {
		logger.debug('Tool get_data called', args);

		const result = await exampleController.getData({
			param: args.param,
		});

		return {
			content: [{ type: 'text' as const, text: result.content }],
		};
	} catch (error) {
		logger.error('Tool get_data failed', error);
		return formatErrorForMcpTool(error);
	}
}

export function register(server: McpServer) {
	server.tool(
		'get_data',
		`PURPOSE: Get data from the example API.
     RETURNS: Markdown with formatted data.
     EXAMPLES: { "param": "value" }`,
		GetDataArgs.shape,
		handleGetData,
	);
}

4. Add CLI Support

Create a CLI command in src/cli/:

// src/cli/example.cli.ts
import { program } from 'commander';
import { Logger } from '../utils/logger.util.js';
import * as exampleController from '../controllers/example.controller.js';
import { handleCliError } from '../utils/error-handler.util.js';

const logger = Logger.forContext('cli/example.cli.ts');

program
	.command('get-data')
	.description('Get example data')
	.option('--param <value>', 'Optional parameter')
	.action(async (options) => {
		try {
			logger.debug('CLI get-data called', options);

			const result = await exampleController.getData({
				param: options.param,
			});

			console.log(result.content);
		} catch (error) {
			handleCliError(error);
		}
	});

5. Register Components

Update the entry points to register your new components:

// In src/cli/index.ts
import '../cli/example.cli.js';

// In src/index.ts (for the tool)
import exampleTool from './tools/example.tool.js';
// Then in registerTools function:
exampleTool.register(server);

Debugging Tools

MCP Inspector

Access the visual MCP Inspector to test your tools and view request/response details:

1. Run npm run dev:server
2. Open http://localhost:5173 in your browser
3. Test your tools and view logs directly in the UI

Server Logs

Enable debug logs for development:

# Set environment variable
DEBUG=true npm run dev:server

# Or configure in ~/.mcp/configs.json

Publishing Your MCP Server

When ready to publish your custom MCP server:

1. Update package.json with your details
2. Update README.md with your tool documentation
3. Build the project: npm run build
4. Test the production build: npm run start:server
5. Publish to npm: npm publish

License

ISC License