FastMCP Demo - TypeScript MCP Server

A demonstration project to understand the Model Context Protocol (MCP) using TypeScript. This project implements a basic MCP server with tools, resources, and prompts.

What is MCP?

The Model Context Protocol (MCP) is a standardized protocol that enables AI assistants to securely access external data sources and tools. It provides a way for AI models to:

• Tools: Execute functions and operations
• Resources: Access data and information
• Prompts: Use predefined prompt templates

Project Structure

fast-mcp/
├── src/
│   └── index.ts          # Main MCP server implementation
├── dist/                 # Compiled JavaScript (generated)
├── package.json          # Project dependencies
├── tsconfig.json         # TypeScript configuration
└── README.md            # This file

Features

This demo server includes:

Tools

• hello: A simple greeting tool that welcomes users
• calculate: Performs basic arithmetic operations (add, subtract, multiply, divide)

Resources

• demo://example: A simple text resource
• demo://config: Server configuration in JSON format

Prompts

• greet_user: Generates a greeting message for a user
• explain_mcp: Provides an explanation of what MCP is

Setup

1. Install dependencies:

   npm install
   

2. Build the project:

   npm run build
   

3. Run the server:

   npm start
   

   Or use the development mode with auto-reload:

   npm run dev
   

How MCP Works

Server Initialization

The server is created with capabilities for tools, resources, and prompts:

const server = new Server(
  { name: "fast-mcp-demo", version: "0.1.0" },
  {
    capabilities: {
      tools: {},
      resources: {},
      prompts: {},
    },
  }
);

Transport

This server uses stdio (standard input/output) transport, which means it communicates via stdin/stdout. This is the most common transport for MCP servers.

Request Handlers

Each capability requires request handlers:

• ListToolsRequestSchema - Lists available tools
• CallToolRequestSchema - Executes a tool
• ListResourcesRequestSchema - Lists available resources
• ReadResourceRequestSchema - Reads a resource
• ListPromptsRequestSchema - Lists available prompts
• GetPromptRequestSchema - Gets a prompt with arguments

Testing with MCP Clients

To test this server, you'll need an MCP client. Popular options include:

1. Claude Desktop - Add the server to your MCP configuration
2. MCP Inspector - A debugging tool for MCP servers
3. Custom MCP Client - Build your own using the MCP SDK

Example Configuration (Claude Desktop)

Add to your Claude Desktop MCP settings:

{
  "mcpServers": {
    "fast-mcp-demo": {
      "command": "node",
      "args": ["/path/to/fast-mcp/dist/index.js"]
    }
  }
}

Learning Path

This project was built incrementally to understand MCP concepts:

1. ✅ Initial Setup - TypeScript configuration and dependencies
2. ✅ Basic Server - Simple server with hello tool
3. ✅ Resources - Added resource reading capabilities
4. ✅ Prompts - Added prompt templates
5. ✅ Advanced Tools - Added calculate tool with error handling

Key Concepts

Tools

Tools are functions that the AI can call. They have:

• A name and description
• An input schema (JSON Schema)
• Execution logic that returns results

Resources

Resources are data sources that can be read. They have:

• A URI identifier
• A name and description
• A MIME type
• Content that can be retrieved

Prompts

Prompts are template messages that can be used to guide AI interactions. They have:

• A name and description
• Optional arguments
• Message templates

Next Steps

To extend this demo, consider:

• Adding file system resources
• Implementing authentication
• Adding more complex tools (API calls, database queries)
• Using different transports (SSE, HTTP)
• Adding logging and error handling middleware
• Implementing caching for resources

Resources

• MCP Specification
• MCP TypeScript SDK
• FastMCP (Python) - The Python equivalent

License

MIT