🔌 Model Context Protocol (MCP) Server

To make our Digital Twin highly interoperable, we expose it using the Model Context Protocol (MCP)—an open standard created by Anthropic. This allows external AI models (like Claude, Gemini, or custom agents) to dynamically discover and use the Digital Twin as a tool workspace.

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💡 What is MCP?

The Model Context Protocol establishes a bidirectional connection between a client (like an IDE or chat window) and a server that exposes:

1. Resources: Static data sources (e.g., system logs or structural schemas).
2. Tools: Executable functions that the model can request to call (with schema verification).
3. Prompts: Standardized templates that steer LLM requests.

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🛠️ Implementing the MCP Server

We implement the MCP server in Python using the official mcp SDK. The server handles communication over Standard Input/Output (stdio), allowing seamless local execution.

import asyncio
from mcp.server.models import InitializationOptions
from mcp.server import Notification, Server
import mcp.types as types
from mcp.server.stdio import stdio_server

# Initialize Server
server = Server("digital-twin-mcp")

@server.list_tools()
async def handle_list_tools() -> list[types.Tool]:
    """Expose available digital twin tools to the client."""
    return [
        types.Tool(
            name="query_digital_twin",
            description="Query the digital twin about system details, projects, and architecture.",
            inputSchema={
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "The question to ask the twin."},
                    "security_check": {"type": "boolean", "default": True}
                },
                "required": ["query"],
            },
        )
    ]

@server.call_tool()
async def handle_call_tool(
    name: str, arguments: dict | None
) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]:
    """Execute tools on request."""
    if name == "query_digital_twin":
        query = arguments.get("query")
        
        # Core retrieval logic call (mocked here)
        result = f"Digital Twin response for: '{query}'. System operates on GraphRAG structure."
        
        return [
            types.TextContent(
                type="text",
                text=result
            )
        ]
    raise ValueError(f"Tool {name} not found")

async def main():
    async with stdio_server() as (read_stream, write_stream):
        await server.run(
            read_stream,
            write_stream,
            InitializationOptions(
                server_name="digital-twin-mcp",
                server_version="1.0.0",
                capabilities=server.get_capabilities(
                    notification_options=Notification(),
                    experimental_capabilities={},
                ),
            ),
        )

if __name__ == "__main__":
    asyncio.run(main())

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⚙️ Connecting MCP to Client Agents

You can link this MCP server to Claude Desktop or any other host system by adding it to your configurations:

{
  "mcpServers": {
    "digital-twin-mcp": {
      "command": "python",
      "args": ["/path/to/digital_twin/mcp_server.py"]
    }
  }
}

Agent-to-Agent Integration

Once configured, an agent running in your workspace can query your twin directly to understand coding standards, system architectures, or security parameters without needing access to raw project folders.