Gaphor MCP Server

Overview

The Gaphor MCP Server is a specialized Model Context Protocol (MCP) service that bridges the gap between textual AI assistants and graphical UML modeling. By exposing Gaphor’s internal model structure through MCP, developers can let AI agents query, modify, and even generate diagrams directly inside their workflow. This eliminates the need to manually translate code or documentation into visual models, enabling a more seamless and automated design process.

Solving the Model‑Code Gap

In many development environments, code evolves faster than its accompanying UML diagrams. Maintaining consistency between source files and visual models becomes tedious and error‑prone. The Gaphor MCP Server solves this by allowing an AI assistant to read the current state of a Gaphor model, compare it against the codebase, and suggest or apply changes. This real‑time validation ensures that architectural diagrams always reflect the latest implementation, reducing regressions and improving documentation quality.

Core Capabilities

• Model interrogation – AI clients can request the current structure of a Gaphor model, retrieving classes, interfaces, relationships, and diagram layout information.
• Element creation & modification – The server accepts MCP commands to add new elements, delete existing ones, or update attributes such as names, stereotypes, and annotations.
• Diagram generation – By combining model data with layout instructions, the server can produce a variety of UML diagrams (class, sequence, activity) on demand.
• Contextual validation – The server can expose rules that compare model elements against code patterns, enabling AI assistants to flag inconsistencies or missing documentation.

These features turn the server into a living representation of design intent that an AI can manipulate as if it were a natural language conversation partner.

Real‑World Use Cases

• Code‑first modeling – A developer writes new code, and the AI automatically updates the Gaphor model to reflect added classes or interfaces.
• Documentation assistance – When generating README files or design docs, the AI can embed live diagram snippets pulled from Gaphor via MCP.
• Model‑driven development – Teams can use AI to enforce architectural constraints, ensuring that any new element added by a developer complies with style guidelines.
• Educational tooling – In learning environments, instructors can ask an AI to walk through a model step‑by‑step, with the server rendering diagrams in real time.

Integration into AI Workflows

Because MCP is language‑agnostic, any Claude or other AI assistant that supports the protocol can interact with Gaphor. A typical workflow involves the AI parsing a user’s natural‑language request (e.g., “Add a class with an attribute”), translating it into MCP commands, and sending them to the server. The server then updates Gaphor’s internal state and returns a confirmation or updated diagram data, which the AI can embed back into the conversation. This tight loop allows developers to iterate on design concepts without leaving their IDE.

Distinctive Advantages

• Native Gaphor integration – Unlike generic diagram tools, the server works directly with Gaphor’s data model, preserving all metadata and layout information.
• Bidirectional sync – AI can both read and write to the model, enabling true collaboration between human intent and automated suggestions.
• Extensibility – Because MCP is modular, future extensions (e.g., custom validation rules or new diagram types) can be added without breaking existing clients.

In summary, the Gaphor MCP Server empowers AI assistants to become active participants in UML modeling, streamlining design consistency, accelerating documentation, and unlocking new possibilities for model‑driven software development.