SysML v2 MCP Server

Systems Engineering Capstone Project

Authors
Affiliations

Andrew Dunn

GitLab Public Sector

Greg Pappas

Department of Defense, Army, AFC-DEVCOM

Dr. Stephen Rapp

Wayne State University, Industrial and Systems Engineering

Published

January 25, 2026

Download as PDF

0.1 Executive Summary

This document outlines the systems engineering plan for developing an open source SysML v2 Model Context Protocol (MCP) server. The project serves dual purposes:

  1. Open Source Contribution: Position GitLab as infrastructure for AI-augmented Model-Based Systems Engineering (MBSE) workflows
  2. Academic Capstone: Demonstrate INCOSE systems engineering principles [1] for a Wayne State University masters engineering capstone project

0.1.1 Key Deliverables

  • Working MCP server with GitLab integration and SysML v2 API support
  • NDIA GVSETS paper (draft March 5, final April)
  • Capstone SE documentation (SEP, SyRS, ADD, VVP)

0.1.2 Timeline

  • Initial Research: Early January 2026 (SysML v2 specifications and prior art)
  • Concept Phase Start: January 12, 2026 (Week 1)
  • Capstone Delivery: April 25, 2026 (Week 15)
  • Duration: 15 weeks

0.2 Problem Statement

The Model Context Protocol [2] ecosystem has 75,000+ GitHub stars and 10+ official SDKs, while SysML v2 [3] achieved OMG adoption in July 2025. Yet their intersection remains unexplored. Defense and aerospace organizations need:

  • Standardized AI-tool integration for MBSE workflows
  • Lightweight programmatic access to SysML v2 models
  • CI/CD integration for model validation
  • Open source alternatives to proprietary vendor lock-in

0.3 MCP for SysML Context

The Model Context Protocol [2] standardizes how AI applications access external data and tools. An MCP server bridges AI assistants and domain-specific systems—in our case, SysML v2 models stored in GitLab.

WITHOUT MCP SERVER:

  ┌──────────────┐                      ┌──────────────────┐
  │   Engineer   │ ─── copy/paste ────▶ │   AI Assistant   │
  │              │ ◀── copy/paste ───── │  (Claude, etc.)  │
  └──────────────┘                      └──────────────────┘
         │                                       │
         ▼                                       ▼
  ┌──────────────┐                      ┌──────────────────┐
  │    GitLab    │    (no connection)   │  Generic SysML   │
  │    .sysml    │                      │  knowledge only  │
  └──────────────┘                      └──────────────────┘

  Problems: AI sees snippets, not full project. Cannot validate.
            Cannot commit. Context lost between conversations.


WITH MCP SERVER:

  ┌──────────────┐       MCP        ┌──────────────────┐
  │   Engineer   │◀─── Protocol ───▶│   AI Assistant   │
  └──────────────┘                  │  (Claude, etc.)  │
                                    └────────┬─────────┘
                                             │
                                             │ MCP
                                             ▼
                                    ┌──────────────────┐
                                    │   SysML v2 MCP   │
                                    │      Server      │
                                    └────────┬─────────┘
                                             │
              ┌──────────────┬───────────────┼───────────────┐
              │              │               │               │
              ▼              ▼               ▼               │
     ┌──────────────┐ ┌─────────────┐ ┌─────────────┐       │
     │    GitLab    │ │  SysML v2   │ │    Local    │       │
     │    .sysml    │ │  API Server │ │    Parser   │       │
     └──────────────┘ └─────────────┘ └─────────────┘

  Benefits: AI reads full project. Validates models. Commits changes.
            Structured understanding. Persists across conversations.
Without MCP With MCP Server
AI sees pasted snippets AI reads entire project
No model validation Validates against SysML v2 spec
Manual copy/paste workflow Direct GitLab integration
Generic SysML knowledge Structured element queries
Context lost between sessions Project state persists

This transforms the AI from a “SysML syntax helper” into an “MBSE collaborator” that understands actual project state and can take actions within it. For detailed MCP architecture and server design, see Section 4.1.

0.4 Project Objectives

  1. Develop an open source MCP server for SysML v2
  2. Integrate with GitLab for model persistence and CI/CD
  3. Connect to SysML v2 API Services for validation
  4. Demonstrate AI-augmented MBSE workflows
  5. Publish findings at NDIA GVSETS

0.5 Scope

0.5.1 In Scope

  • MCP server implementation (Go)
  • GitLab file read/write operations
  • SysML v2 API client integration
  • stdio and HTTP transport mechanisms
  • Container deployment
  • Documentation and examples

0.5.2 Out of Scope (Future Work)

  • AI benchmarking framework
  • Multi-agent architectures
  • Commercial integrations
  • Full SysML v2 parser implementation

0.6 Document Structure

This book contains the complete systems engineering documentation:

  • Chapter 1: SysML v2 background
  • Chapter 2: Upstream research and prior art
  • Chapter 3: Model Context Protocol
  • Chapter 4: Systems Engineering Plan (SEP)
  • Chapter 5: Work Breakdown Structure (WBS)
  • Chapter 6: Stakeholder Analysis
  • Chapter 7: System Requirements Specification (SyRS)
  • Chapter 8: Architecture Design Description (ADD)
  • Chapter 9: Verification & Validation Plan (VVP)
  • Chapter 10: Implementation
  • Chapter 11: Conclusions

Appendices include glossary, references, and traceability matrix.