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144 lines
3.0 KiB
144 lines
3.0 KiB
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# MCP Integration Demo Script
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## 1. Setup & Architecture Overview
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```bash
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# Terminal 1: Start Stock Server
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python examples/mcp_example/mock_stock_server.py
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# Terminal 2: Start Math Server
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python examples/mcp_example/mock_math_server.py
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# Terminal 3: Start Multi-Agent System
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python examples/mcp_example/mock_multi_agent.py
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```
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## 2. Key Components
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### Server-Side:
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- FastMCP servers running on ports 8000 and 8001
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- Math Server provides: add, multiply, divide operations
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- Stock Server provides: price lookup, moving average calculations
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### Client-Side:
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- Multi-agent system with specialized agents
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- MCPServerSseParams for server connections
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- Automatic task routing based on agent specialization
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## 3. MCP Integration Details
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### Server Implementation:
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```python
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# Math Server Example
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from fastmcp import FastMCP
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mcp = FastMCP("Math-Server")
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@mcp.tool()
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def add(a: int, b: int) -> int:
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"""Add two numbers together"""
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return a + b
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```
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### Client Integration:
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```python
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from swarms.tools.mcp_integration import MCPServerSseParams
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# Configure MCP server connection
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server = MCPServerSseParams(
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url="http://0.0.0.0:8000",
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headers={"Content-Type": "application/json"}
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)
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# Initialize agent with MCP capabilities
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agent = Agent(
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agent_name="Calculator",
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mcp_servers=[server],
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max_loops=1
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)
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```
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## 4. Demo Flow
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1. Math Operations:
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```
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Enter a math problem: 5 plus 3
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Enter a math problem: 10 times 4
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```
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2. Stock Analysis:
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```
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Enter a math problem: get price of AAPL
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Enter a math problem: calculate moving average of [10,20,30,40,50] over 3 periods
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```
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## 5. Integration Highlights
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1. Server Configuration:
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- FastMCP initialization
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- Tool registration using decorators
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- SSE transport setup
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2. Client Integration:
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- MCPServerSseParams configuration
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- Agent specialization
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- Task routing logic
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3. Communication Flow:
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- Client request → Agent processing → MCP server → Response handling
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4. Error Handling:
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- Graceful error management
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- Automatic retry mechanisms
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- Clear error reporting
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## 6. Code Architecture
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### Server Example (Math Server):
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```python
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@mcp.tool()
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def add(a: int, b: int) -> int:
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"""Add two numbers together"""
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return a + b
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```
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### Client Example (Multi-Agent):
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```python
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calculator = MathAgent("Calculator", "http://0.0.0.0:8000")
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stock_analyst = MathAgent("StockAnalyst", "http://0.0.0.0:8001")
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```
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## 7. Key Benefits
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1. Modular Architecture
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2. Specialized Agents
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3. Clean API Integration
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4. Scalable Design
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5. Standardized Communication Protocol
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6. Easy Tool Registration
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7. Flexible Server Implementation
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## 8. Testing & Validation
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1. Basic Connectivity:
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```python
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def test_server_connection():
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params = {"url": "http://0.0.0.0:8000"}
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server = MCPServerSse(params)
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asyncio.run(server.connect())
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assert server.session is not None
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```
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2. Tool Execution:
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```python
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def test_tool_execution():
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params = {"url": "http://0.0.0.0:8000"}
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function_call = {
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"tool_name": "add",
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"arguments": {"a": 5, "b": 3}
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}
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result = mcp_flow(params, function_call)
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assert result is not None
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```
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