swarms/examples/mcp_example/presentation_script.md

MCP Integration Demo Script

1. Setup & Architecture Overview

# Terminal 1: Start Stock Server
python examples/mcp_example/mock_stock_server.py

# Terminal 2: Start Math Server
python examples/mcp_example/mock_math_server.py

# Terminal 3: Start Multi-Agent System
python examples/mcp_example/mock_multi_agent.py

2. Key Components

Server-Side:

• FastMCP servers running on ports 8000 and 8001
• Math Server provides: add, multiply, divide operations
• Stock Server provides: price lookup, moving average calculations

Client-Side:

• Multi-agent system with specialized agents
• MCPServerSseParams for server connections
• Automatic task routing based on agent specialization

3. Demo Flow

1. Math Operations:

Enter a math problem: 5 plus 3
Enter a math problem: 10 times 4

2. Stock Analysis:

Enter a math problem: get price of AAPL
Enter a math problem: calculate moving average of [10,20,30,40,50] over 3 periods

4. Integration Highlights

1. Server Configuration:

• FastMCP initialization
• Tool registration using decorators
• SSE transport setup

2. Client Integration:

• MCPServerSseParams configuration
• Agent specialization
• Task routing logic

3. Communication Flow:

• Client request → Agent processing → MCP server → Response handling

5. Code Architecture

Server Example (Math Server):

@mcp.tool()
def add(a: int, b: int) -> int:
    """Add two numbers together"""
    return a + b

Client Example (Multi-Agent):

calculator = MathAgent("Calculator", "http://0.0.0.0:8000")
stock_analyst = MathAgent("StockAnalyst", "http://0.0.0.0:8001")

6. Key Benefits

1. Modular Architecture
2. Specialized Agents
3. Clean API Integration
4. Scalable Design