Message Communication Protocol (MCP) Implementations

This repository demonstrates two implementations of the Model Context Protocol (MCP) by Anthropic AI, a JSON-RPC based protocol for bi-directional communication that enables developers to build secure, two-way connections between their data sources and AI-powered tools.

1. Memory Stream Implementation (run on same host)

Overview

The Memory Stream implementation demonstrates local client-server communication using in-memory channels. Both client and server operate within the same host.

Protocol Flow

Client                          Server
   |                              |
   |------ initialize() --------->|  (sends capabilities)
   ||
   ||
   ||
   ||
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   |
        
            
                ### Components

1. **Server** (`websocket/server_ws.py`)
```python
@weather_server.list_tools()
async def handle_list_tools():
    return [
        Tool(
            name="get-alerts",
            description="Get active weather alerts for a US state",
            inputSchema={
                "type": "object",
                "required": ["state"],
                "properties": {
                    "state": {
                        "type": "string",
                        "description": "Two-letter state code (e.g. CA, NY)"
                    }
                }
            }
        ),
        Tool(
            name="get-forecast",
            description="Get weather forecast for a location",
            inputSchema={
                "type": "object",
                "required": ["latitude", "longitude"],
                "properties": {
                    "latitude": {
                        "type": "number",
                        "description": "Latitude of the location"
                    },
                    "longitude": {
                        "type": "number",
                        "description": "Longitude of the location"
                    }
                }
            }
        )
    ]

2. Client (websocket/client_ws.py)

client = WebSocketClient("ws://localhost:8000/ws")
await client.connect()
tools = await client.list_tools()
forecast = await client.call_tool("get-forecast", 
    {"latitude": 37.7749, "longitude": -122.4194})

Available Weather Tools

• get-alerts: Get active weather alerts for a US state (requires two-letter state code)
• get-forecast: Get detailed weather forecast for a location (requires latitude and longitude)

Setup

Memory Stream Run (same host via MemoryStream)

pip install -r requirements.txt
cd memory_stream
python test.py

Websocket Run (client and server on different hosts)

pip install -r requirements.txt
cd websocket
python test_ws.py

            Test execution process:

1. Server is started in a separate process
2. System waits 2 seconds to ensure server initialization
3. Client connects and executes test requests via FastAPI WebSocket port 8000
4. Server process is gracefully terminated after tests

To run server client on two machins, you can use run_server.py and run_client.py script.

Additional Resources

For a detailed analysis of the client-server communication [port 8000], a PCAP (Packet Capture) file is available in the repository. This capture contains the complete WebSocket conversation between client and server components. This implementation is not encrypted, so the data is transmitted in plain text. The PCAP file can be opened using Wireshark, a popular network protocol analyzer. It provides a comprehensive view of the communication, including the request headers and their content.

Download PCAP File