Demo MCP Server

Demo MCP Server

A demonstration server designed to showcase core Model Context Protocol (MCP) primitives including tools, resources, and prompts for presentations. It provides functional examples like text analysis and financial calculations to illustrate how AI models interact with external functions and data.

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Demo MCP Server

A simple but complete MCP (Model Context Protocol) server built for presentations and demonstrations. This server showcases all three core MCP primitives: Tools, Resources, and Prompts in under 240 lines of Python code.

For Presenters: See DEMO_CHEATSHEET.md for a detailed 5-minute presentation script with timing guide.

What This Demonstrates

This demo server showcases:

  1. Tools (Model-controlled functions)

    • calculate_tip - Financial calculations
    • analyze_text - Text analysis and statistics
    • convert_temperature - Temperature conversion
  2. Resources (Application-controlled data)

    • demo://server-info - Server metadata and capabilities
    • demo://example-data - Sample data for testing
    • demo://statistics - Usage statistics
  3. Prompts (User-controlled templates)

    • demo_workflow - Guided walkthrough of all capabilities
    • quick_demo - 2-minute quick demonstration

Quick Start

Prerequisites

pip install fastmcp

For MCP Inspector (requires Node.js):

npm install -g @modelcontextprotocol/inspector

Option 1: Interactive Demo with MCP Inspector (Recommended)

Linux/Mac:

cd mcp-demo
./demo.sh
# Choose option 1 for interactive demo

Windows/Direct:

npx @modelcontextprotocol/inspector python demo_server.py

This opens a web UI where you can:

  • Browse available tools, resources, and prompts
  • Test tools with custom inputs
  • See real-time JSON responses
  • Perfect for live presentation demos

Option 2: Use with Claude Desktop

  1. Add to your Claude Desktop config:
    • Mac: ~/Library/Application Support/Claude/claude_desktop_config.json
    • Windows: %APPDATA%\Claude\claude_desktop_config.json
{
  "mcpServers": {
    "demo-server": {
      "command": "python",
      "args": ["D:\\Demo\\mcp-demo\\demo_server.py"]
    }
  }
}
  1. Restart Claude Desktop completely
  2. Look for the hammer icon - server tools will be available in conversations
  3. The server should auto-start when Claude Desktop launches

Option 3: Standalone Server

python demo_server.py

The server will start and display:

  • 3 available tools
  • 3 resources
  • 2 prompts

Connect via any MCP-compatible client (Claude Desktop, Cursor, VS Code with MCP extension).

Demo Script for Presentation

Setup (30 seconds before demo)

cd mcp-demo
npx @modelcontextprotocol/inspector python3 demo_server.py

Demo Flow (5 minutes)

1. Introduction (30 seconds)

Show the code:

  • Open demo_server.py
  • Highlight the decorators: @mcp.tool(), @mcp.resource(), @mcp.prompt()
  • Point out: "150 lines of Python, zero boilerplate"

Say:

"This is a complete MCP server. Notice how simple it is - just Python functions with decorators. Type hints automatically generate schemas, docstrings become descriptions for the LLM."

2. Tools Demo (2 minutes)

In MCP Inspector:

  • Click on "Tools" tab
  • Show the three available tools

Live Demo - calculate_tip:

{
  "bill_amount": 100,
  "tip_percentage": 20
}

Say:

"Tools are model-controlled - the LLM decides when to invoke them based on user requests. Here we calculate a 20% tip on $100. See the structured JSON response."

Live Demo - analyze_text:

{
  "text": "The Model Context Protocol enables AI applications to seamlessly connect with external tools and data sources."
}

Say:

"Same pattern - clean input, structured output. The LLM can use this data to provide intelligent responses."

3. Resources Demo (1 minute)

In MCP Inspector:

  • Click on "Resources" tab
  • Fetch demo://server-info

Say:

"Resources are application-controlled data sources. Unlike tools where the LLM decides, here the client application determines when to fetch data. Think of them like REST GET endpoints - they provide context to the AI."

Show the JSON response with server capabilities

4. Prompts Demo (1 minute)

In MCP Inspector:

  • Click on "Prompts" tab
  • Show demo_workflow and quick_demo

Say:

"Prompts are user-controlled templates. In a real application, these would appear as slash commands or menu items. They help users accomplish common tasks without remembering exact phrasing."

Trigger the quick_demo prompt and show how it structures the interaction

5. Wrap Up (30 seconds)

Say:

"From concept to working server: minutes, not hours. This same server works with Claude Desktop, Cursor, VS Code - any MCP-compatible client. Build once, use everywhere. That's the power of MCP."

Key Talking Points

During Code Review

  • "Type hints auto-generate JSON schemas"
  • "Docstrings become LLM-readable descriptions"
  • "Zero configuration - just decorators and functions"

During Tool Demo

  • "LLM autonomously decides when to call these"
  • "Structured inputs and outputs"
  • "Error handling built into the protocol"

During Resources Demo

  • "Application controls when to fetch"
  • "Real-time data without tool invocation overhead"
  • "Perfect for context that changes frequently"

During Prompts Demo

  • "User initiates via UI or commands"
  • "Reusable templates for common workflows"
  • "Guides users through complex interactions"

Closing

  • "Three primitives cover all integration needs"
  • "Build once, deploy everywhere"
  • "10,000+ servers in the ecosystem already"

Testing the Demo

Test calculate_tip

# In MCP Inspector or programmatically
calculate_tip(85.50, 18)

# Expected output:
{
  "bill_amount": 85.5,
  "tip_percentage": 18,
  "tip_amount": 15.39,
  "total": 100.89,
  "split_2_people": 50.45
}

Test analyze_text

analyze_text("Building MCP servers is straightforward with official SDKs.")

# Expected output:
{
  "word_count": 8,
  "character_count": 60,
  "character_count_no_spaces": 52,
  "estimated_reading_time_minutes": 0.0,
  "analyzed_at": "2025-01-23T..."
}

Test convert_temperature

convert_temperature(25, "C", "F")

# Expected output:
{
  "original_value": 25,
  "original_unit": "C",
  "converted_value": 77.0,
  "converted_unit": "F",
  "formula_used": "C → C → F"
}

Supports conversion between Celsius (C), Fahrenheit (F), and Kelvin (K) in any direction.

Requirements

Python Dependencies:

pip install fastmcp

For MCP Inspector (Optional):

  • Node.js 16+ required
  • Install: npm install -g @modelcontextprotocol/inspector
  • Or use directly: npx @modelcontextprotocol/inspector

Tested with:

  • Python 3.10+
  • FastMCP 0.2.0+
  • Claude Desktop (latest)

Learning Points for Audience

After this demo, your audience will understand:

  1. How simple MCP servers are to build - Less than 200 lines for a full-featured server
  2. The three core primitives - Tools, Resources, Prompts and when to use each
  3. Type-driven development - Python types become API contracts
  4. Instant testing - MCP Inspector provides immediate feedback
  5. Portability - Same server works across all MCP clients

Troubleshooting

"ModuleNotFoundError: No module named 'fastmcp'"

pip install fastmcp

"npx command not found"

Install Node.js from https://nodejs.org/

Server not appearing in Claude Desktop

  1. Check config path:
    • Mac: ~/Library/Application Support/Claude/claude_desktop_config.json
    • Windows: %APPDATA%\Claude\claude_desktop_config.json
  2. Verify Python path: Use absolute path to demo_server.py
  3. Check Python command: Use python or python3 depending on your system
  4. Restart Claude Desktop: Completely quit and relaunch
  5. Check logs: Look for errors in Claude Desktop developer console

Server starts but tools not available

  • Look for the hammer icon in Claude Desktop chat interface
  • Server must successfully connect (check for errors in terminal)
  • Try manually running: python demo_server.py to see startup messages

Notes for Presenter

  • Timing: Practice to keep it under 5 minutes
  • Backup: Have screenshots ready if live demo has issues
  • Questions: Be ready to show the source code on request
  • Transition: After demo, move to CV-Forge as "more complex real-world example"

Additional Resources

  • Official MCP Documentation: https://modelcontextprotocol.io
  • FastMCP GitHub: https://github.com/jlowin/fastmcp
  • MCP Specification: https://spec.modelcontextprotocol.io
  • MCP Servers Registry: https://github.com/modelcontextprotocol/servers
  • Claude Desktop: https://claude.ai/download

Project Structure

mcp-demo/
├── demo_server.py           # Main MCP server implementation
├── requirements.txt         # Python dependencies
├── demo.sh                  # Interactive demo launcher (Linux/Mac)
├── claude_desktop_config.json  # Example Claude Desktop config
├── README.md                # This file
├── DEMO_CHEATSHEET.md       # Presentation script with timing
└── .groupcode/              # Code organization metadata

License

MIT License - Feel free to use this as a template for your own MCP servers.


Demo Server Version: 1.0.0
Built with: FastMCP + Python 3.10+
Created for: MCP Presentations and Learning

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