learn-mcp

A learning MCP (Model Context Protocol) project built with the official TypeScript SDK. It contains both:

• a server (src/index.ts) that exposes tools, resources, prompts, and sampling to AI clients like Cursor, and

• a CLI client (src/client.ts) that connects to the server and lets you interact with those capabilities from the terminal, including LLM-powered queries via the Vercel AI SDK.

Data is stored in a local JSON file (src/data/users.json) — no external database required.

Features

Tools

Resources

Prompts

Related MCP server: simple_mcp

Prerequisites

• Node.js 18+

• npm

Getting started

# Install dependencies
npm install

# Build TypeScript (optional — Cursor config uses tsx directly)
npm run build

Scripts

Cursor setup

This project includes .cursor/mcp.json:

{
  "mcpServers": {
    "learn-mcp": {
      "type": "stdio",
      "command": "npx",
      "args": ["tsx", "src/index.ts"],
      "cwd": "${workspaceFolder}"
    }
  }
}

After opening the project in Cursor:

1. Go to Settings → MCP and confirm learn-mcp is connected

2. Use Agent mode to invoke tools and read resources

3. Reload the window if the server doesn't appear (Ctrl+Shift+P → "Developer: Reload Window")

Example prompts in Cursor

Create a user named Alice with email alice@example.com, address 42 Oak St, phone 555-1234
Show me all users
Read users://5/profile
Use the generate-fake-name prompt with name "Jared Leto"

CLI client

src/client.ts is a standalone MCP client that spawns the server over stdio and gives you an interactive terminal menu. It's a hands-on way to exercise every capability without Cursor or the Inspector.

# Requires GOOGLE_API_KEY in .env for the Query and sampling features
npm run client:dev

The menu offers:

The client also advertises the sampling capability, so server-initiated sampling/createMessage requests (used by create-random-user) are fulfilled locally — with a confirmation prompt before any LLM call runs.

Requires a GOOGLE_API_KEY in .env. The Query and sampling features call the Google Generative AI API via the Vercel AI SDK.

MCP Inspector

Test tools, resources, and prompts without Cursor:

npm run inspect

Open the URL printed in the terminal (usually http://localhost:6274).

Stdio rule: Never run the server through npm run dev in MCP config — npm prints to stdout and breaks the JSON-RPC transport. Always use tsx src/index.ts or node build/index.js directly.

Project structure

├── .cursor/
│   └── mcp.json          # Cursor MCP configuration
├── src/
│   ├── index.ts          # Server entry point
│   ├── client.ts         # Interactive CLI client
│   └── data/
│       └── users.json    # User "database"
├── build/                # Compiled output (gitignored)
├── package.json
├── tsconfig.json
└── README.md

Transport

Uses stdio — the client spawns the server process and communicates over stdin/stdout. This is the standard setup for local MCP servers in Cursor and Claude Desktop.

Sampling note

create-random-user calls sampling/createMessage, which asks the client (not the server) to run an LLM and return generated text. This requires the client to support the sampling capability.

• Works when the client implements sampling/createMessage and the user approves the request

• Cursor may not fully support sampling yet — if create-random-user fails with Method not found, use create-user instead

Development notes

• Use registerTool(), registerResource(), and registerPrompt() — the older .tool(), .resource(), and .prompt() methods are deprecated

• Resource templates need a list callback for Cursor to discover individual URIs

• Use console.error() for debug logs — never console.log() on stdio transport (stdout is reserved for JSON-RPC)

• McpServer constructor takes two arguments: server info (name, version) and options (capabilities, etc.)

License

ISC