fujitsu-sdt-mcp

fujitsu-sdt-mcp

1
research_and_data

The Fujitsu Social Digital Twin MCP Server integrates Fujitsu's Digital Rehearsal API with the Model Context Protocol, providing natural language access for Large Language Models to simulate and analyze human and social behaviors digitally. Its primary features include running simulations, managing simulation data, and generating configurations from natural language.

Overview

Fujitsu's Social Digital Twin recreates not only the state of people and objects in the digital space based on real-world data, but also entire economic and social activities. Its core function, "Digital Rehearsal," enables users to simulate human and social behavior in a digital space before implementing measures in the real world. This project uses MCP to bridge the gap between LLMs and the Digital Rehearsal API, enabling users to run simulations and analyze results using natural language.

Key Features

  • Retrieve and display simulation lists
  • Start simulations
  • Retrieve and analyze simulation results
  • Manage simulation data
  • Analyze traffic simulations
  • Compare scenarios
  • Generate simulation configurations from natural language

Prerequisites

  • Python 3.13 or higher
  • Access to Fujitsu API Gateway (API Key)
  • MCP-compatible LLM client (e.g., Claude Desktop)

Usage

Starting the MCP Server

The server communicates using standard I/O with MCP clients.

Using the Interactive Client

A simple client is provided for direct interaction with the MCP server. When you start the client, a list of available resources and tools will be displayed, and you can call them from the command line.

Resources and Tools

Resources:

  • digital_rehearsal_overview: Overview of Digital Rehearsal technology
  • simulation_metrics_explanation: Explanation of simulation metrics
  • scenario_examples: Example scenarios

Tools:

  • list_simulations: Retrieve a list of simulations
  • start_simulation: Start a simulation
  • get_simulation_result: Retrieve simulation results
  • get_metrics: Retrieve simulation metrics
  • list_simdata: Retrieve a list of simulation data
  • get_simdata: Retrieve simulation data
  • analyze_traffic_simulation: Analyze traffic simulation
  • compare_scenarios: Compare scenarios
  • create_natural_language_simulation_config: Generate simulation settings from natural language

Contributing

Please report bugs or feature requests via GitHub Issues. Pull requests are welcome.

License

This project is released under the MIT License. See the LICENSE file for details.