8. Use Cases
The following use cases illustrate how the MCP Server can support core architecture activities across analysis, dependency investigation, governance, transformation planning, and interpretation of architecture views. They show how AI-driven access to Hopex repository knowledge can help architects explore relationships across domains, synthesize architecture information more efficiently, and produce more consistent inputs for architecture review and decision-making.
8.1. Analysis and Investigation
Faster impact analysis: Architects can assess the consequences of changing or retiring an application, process, technology, or capability by exploring related objects and dependencies.Sample prompt: "Assess the impact of retiring application X".
More efficient dependency investigation: Architects can identify how applications depend on other applications, technologies, processes, or data elements to investigate issues and understand the broader landscape.Sample prompt: "Identify the applications and technologies on which application X depends."
Clearer capability-driven analysis: Architects can move from business capabilities to supporting applications, processes, projects, and technologies to understand alignment between strategy and architecture.Sample prompt: "Identify the applications, processes, and projects that support business capability X."
Faster preparation for architecture reviews: AI agents can summarize architecture knowledge to help architects prepare review materials and answer stakeholder questions more quickly.Sample prompt: "Summarize the main architecture elements and issues related to domain X for a review meeting."
8.2. Reporting, Governance, and Risk
Dashboarding and reporting support: AI agents can prepare architecture dashboards and reports by summarizing repository data and highlighting key indicators for stakeholders.Sample prompt: "Prepare a summary for an architecture dashboard on application obsolescence and related business impacts."
Governance KPI monitoring: AI agents can monitor governance KPIs by highlighting trends, gaps, and non-compliance indicators across the portfolio.Sample prompt: "Identify the main governance KPIs for the application portfolio and highlight the areas that require attention."
Technology risk tracking: AI agents can identify technology risks such as obsolete technologies, unsupported components, critical dependencies, or concentrations of risk across the landscape.Sample prompt: "Identify the technologies in the portfolio that present the highest risk due to obsolescence, dependency exposure, or critical usage."
8.3. Transformation Support
Stronger decision support for transformation: AI agents can connect strategic, business, and IT elements to support architecture decisions and evaluate transformation scenarios.Sample prompt: "Identify the business capabilities, applications, and technologies involved in transformation initiative X."
Transformation roadmap support: AI agents can help architects structure transformation roadmaps by identifying impacted domains, dependencies, and sequencing constraints across initiatives.Sample prompt: "Outline the main phases and dependencies for transforming application domain X."
Migration scenario analysis: AI agents can compare migration or modernization scenarios by highlighting affected capabilities, applications, technologies, and potential impacts on the target architecture.Sample prompt: "Compare two migration scenarios for application X and highlight the main impacts and dependencies."
8.4. Visual Insight
Deriving insights from architecture views: When diagrams are available in an interpretable format, AI agents can derive key elements, visible relationships, and structural insights from architecture views.Sample prompt: "Analyze this architecture diagram and identify the main components and relationships."
Visual comparison of architecture views: AI agents can compare two architecture diagrams to highlight key differences, changed relationships, or structural evolutions between views.Sample prompt: "Compare these two architecture diagrams and highlight the main structural differences."
Taken together, these use cases show that the MCP Server can support a broad range of architecture work, from impact analysis and dependency mapping to governance support, transformation assessment, and interpretation of architecture views. By making Hopex repository knowledge easier to query and synthesize through AI-driven interaction, it strengthens the architect's ability to investigate the current landscape, assess change, and provide structured insights to support enterprise architecture decisions.