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MCP Server Boilerplate

by ricleedo

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 Desktop, Cursor, Claude Code, Gemini, and 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

Related MCP server: MCP Server Boilerplate

Features

  • Two example tools: "hello-world" and "get-mcp-docs"

  • TypeScript support with ES2022 target and ES modules

  • Multi-client installation scripts (Claude Desktop, Cursor, Claude Code, Gemini, etc.)

  • Automatic npm publishing workflow

  • Environment variable support via .env.local

  • Clean project structure with Zod validation

How It Works

This MCP server template provides:

  1. A basic server setup using the MCP SDK

  2. Example tool implementation

  3. Build and installation scripts

  4. 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

You can use this MCP server directly without cloning:

# Run the server directly with npx
npx @r-mcp/boilerplate

Option 2: Customize and Develop

# 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:

# Install to all MCP clients (Claude Desktop, Cursor, Claude Code, Gemini, MCP)
pnpm run install-server

# Install to specific clients
pnpm run install-desktop       # Claude Desktop
pnpm run install-cursor        # Cursor IDE
pnpm run install-code          # Claude Code CLI
pnpm run install-code-library  # Claude Code Library (~/.claude/mcp-library/)
pnpm run install-mcp           # Local .mcp.json for development

# You can also combine multiple targets:
node scripts/update-config.js cursor code desktop

These scripts will:

  • Build the project automatically (TypeScript compilation + chmod permissions)

  • Configure clients to use npx @r-mcp/<directory-name>@latest (auto-updating)

  • Only the local .mcp.json uses the development version (node dist/index.js)

  • Include environment variables from .env.local if present

Publishing Your Server

To publish your customized MCP server:

# Build, commit, and publish to npm in one command
pnpm run release

This script (scripts/build-and-publish.js) will:

  1. Commit any pending changes first

  2. Update package name to @r-mcp/<directory-name>

  3. Update bin name to match directory

  4. Increment patch version automatically

  5. Build the TypeScript project

  6. Commit version bump to git

  7. Push to remote repository

  8. Publish to npm with public access

Usage with MCP Clients

The installation scripts automatically configure your MCP clients. For reference, here's what gets added:

Production Clients (Claude Desktop, Cursor, Claude Code, Gemini):

{
  "mcpServers": {
    "boilerplate": {
      "command": "npx",
      "args": ["-y", "@r-mcp/boilerplate@latest"],
      "env": {
        // Environment variables from .env.local are included here
      }
    }
  }
}

Local Development (.mcp.json):

{
  "mcpServers": {
    "boilerplate": {
      "command": "node",
      "args": ["/absolute/path/to/dist/index.js"],
      "env": {
        // Environment variables from .env.local are included here
      }
    }
  }
}

After running installation scripts, restart your MCP client 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 MCP server implementation
├── scripts/
│   ├── update-config.js      # Multi-client configuration installer
│   └── build-and-publish.js  # Automated npm publishing workflow
├── dist/                     # Compiled JavaScript (generated)
├── package.json              # Project configuration
├── tsconfig.json             # TypeScript configuration
├── CLAUDE.md                 # Claude Code instructions
├── .env.local                # Environment variables (optional)
└── README.md                 # This file

Development Workflow

Local Development

  1. Make changes to src/index.ts

  2. Run pnpm run build to compile TypeScript

  3. Test your server with pnpm start

  4. Use pnpm run install-mcp for local testing

  5. Restart your MCP client to load changes

Publishing Updates

  1. Test your changes locally

  2. Run pnpm run release to publish to npm

  3. Clients using npx @r-mcp/<your-package>@latest auto-update

  4. No client reconfiguration needed

Environment Variables

Create a .env.local file for environment-specific configuration:

# .env.local
API_KEY=your-api-key
DATABASE_URL=your-database-url

These variables are automatically included in MCP server configurations during installation.

Next Steps

  1. Fork or clone this boilerplate

  2. Customize the server name and tools in src/index.ts

  3. Add your own tools, resources, and prompts

  4. Configure environment variables in .env.local

  5. Run pnpm run release to publish your server

  6. Install to clients with pnpm run install-server

License

MIT

Available Tools

1 tool
make-html-pageB

Generate an HTML page using GPT-5 and save it to a file path

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.1/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool 'generate[s] an HTML page using GPT-5 and save[s] it to a file path', which implies it performs write operations and uses AI, but lacks details on permissions, error handling, rate limits, or what the 'save' entails (e.g., overwriting files). This is insufficient for a tool with potential side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core functionality without any wasted words. It directly states the action and outcome, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of generating and saving files with AI, the description is incomplete. It lacks annotations, has no output schema, and doesn't explain behavioral aspects like how the HTML is generated, file path requirements, or error cases. This leaves significant gaps for an AI agent to understand the tool's full context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description does not mention any parameters, which is appropriate here since none exist. A baseline of 4 is applied as it correctly avoids redundant information, though it doesn't add value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('generate', 'save') and resources ('HTML page', 'file path'), and mentions the use of 'GPT-5'. However, since there are no sibling tools, the lack of differentiation doesn't reduce the score from the maximum clarity for its standalone purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, prerequisites, or constraints. It merely states what the tool does without context for its application, which is a significant gap in usage guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

TDQS

B3.2/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap with other tools. The tool 'make-html-page' has a clear, singular purpose that cannot be confused with any other functionality in this set.

Naming Consistency5/5

The single tool name 'make-html-page' follows a consistent verb-noun pattern, using kebab-case. Since there is only one tool, there is no inconsistency to evaluate, and the naming convention is straightforward and appropriate.

Tool Count2/5

A single tool is too few for a server named 'MCP Server Boilerplate', which implies a broader or more foundational purpose. This minimal set feels thin and underdeveloped for typical boilerplate functionality, which might include multiple utilities or examples.

Completeness2/5

The tool surface is severely incomplete for a boilerplate server. It only covers generating HTML pages, lacking any other common operations like file management, data processing, or configuration handling that would be expected in a boilerplate context, leading to significant gaps in functionality.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

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