Model Control Protocol Server

Overview

The Mase2 MCP Server is a dedicated Model Control Protocol (MCP) implementation that exposes a RESTful interface for managing the full lifecycle of service‑related data. By abstracting complex domain concepts such as assets, onboarding workflows, and brick configurations into well‑defined endpoints, the server removes the need for developers to build custom data stores or orchestration layers when integrating AI assistants with enterprise systems.

At its core, the server addresses a common pain point: how to keep an AI assistant’s knowledge in sync with real‑world operational data. It does this by providing CRUD endpoints for assets, service offerings, onboarding tasks, and brick configurations—all of which are common entities in a modern cloud‑native service marketplace. Developers can query, create, update, or delete these resources through a single, versioned API (), ensuring that the assistant’s context remains consistent with the underlying data store.

Key capabilities include:

• Asset Management – Store detailed attributes, relationships, and inventory checks for physical or logical assets. This allows an assistant to answer questions about asset status, availability, and compliance.
• Service Offerings & Types – Define what services are available, their metadata, and how they map to broader service categories. The API supports full lifecycle operations so that new offerings can be rolled out without downtime.
• Onboarding Tasks – Capture the steps required to bring a new asset or service into production, including metadata such as owners, due dates, and status. This is critical for operational workflows that rely on AI guidance during deployment.
• Brick Configurations – Manage reusable configuration blocks (“brick combinations”) that compose complex service setups. This feature is especially valuable in environments where services are built from modular components.

In practice, a developer can hook the MCP Server into an AI workflow by configuring the assistant’s context to query these endpoints. For example, a conversational agent could ask, “What is the current status of asset X?” and receive an up‑to‑date answer by fetching from the endpoint. Similarly, when a new service is launched, the assistant can automatically pull its definition from and present it to users without manual data entry.

The server’s design offers several standout advantages:

• Versioned API – Future changes can be introduced in new minor versions without breaking existing integrations.
• TypeScript Foundations – Strong typing reduces runtime errors and improves developer ergonomics when interacting with the API.
• Modular Structure – Each domain area is isolated in its own file, making it straightforward to extend or replace parts of the system.
• Open‑Source Licensing – The MIT license encourages adoption and community contributions, ensuring that the server can evolve with industry needs.

Overall, the Mase2 MCP Server provides a ready‑to‑use, scalable foundation for any project that needs an AI assistant to interact seamlessly with structured service data. By centralizing asset, onboarding, and configuration management behind a single protocol, it empowers developers to build richer, contextually aware AI experiences with minimal overhead.