MasterGo Magic MCP

MasterGo Magic MCP is a lightweight, self‑contained Model Context Protocol server that bridges MasterGo’s design platform with AI assistants such as Claude. By exposing a simple HTTP API, it allows an assistant to query MasterGo design files and receive their Domain‑Specific Language (DSL) representations in real time. This eliminates the need for custom plugins or manual data exports, enabling developers to treat design artifacts as first‑class data sources within conversational AI workflows.

The server solves a common pain point for design teams: accessing up‑to‑date component definitions, layout structures, and collaboration metadata directly from the AI model. Instead of copying JSON snippets or manually refreshing design files, an assistant can request a component’s DSL via the MCP tool and receive an authoritative representation. This is especially valuable when building AI‑augmented design assistants that can suggest code snippets, validate design constraints, or generate documentation on the fly.

Key capabilities include:

• Direct DSL retrieval from MasterGo files using a secure API token.
• Zero external dependencies beyond Node.js, making deployment straightforward in CI/CD pipelines or local environments.
• Rule‑based filtering via the flag, allowing fine‑grained control over which design elements are exposed.
• Debug and environment variable support for seamless integration into existing tooling ecosystems.

Typical use cases span from automated design‑to‑code pipelines—where an AI assistant translates MasterGo components into framework‑specific code—to real‑time design validation, where the model can query constraints and flag violations as a user edits. In educational settings, tutors can use the MCP to fetch design examples and generate explanatory prompts.

Integrating MasterGo Magic MCP into an AI workflow is straightforward: the server runs as a local or remote service, and the assistant invokes it via an MCP tool. Once authenticated with a MasterGo API token, the assistant can issue queries such as “What is the DSL for component X?” and receive a structured response that can be parsed or fed into downstream LLM prompts. This tight coupling empowers developers to build sophisticated, context‑aware AI helpers that operate directly on live design data without cumbersome middleware.