MCP Server Spring Demo

The Mcp Server Spring Demo demonstrates how to build a lightweight MCP (Model Context Protocol) server using Spring Boot and the ai module. At its core, the server exposes a set of RESTful endpoints that conform to the MCP specification, allowing an AI assistant such as Claude to query for resources, invoke tools, fetch prompts, and control sampling parameters. By running this demo, developers can see how the protocol’s declarative interface translates into concrete Spring MVC controllers and service layers, providing a clear reference for building production‑grade MCP backends.

The primary problem this server solves is the disconnect between AI assistants and domain‑specific data or services. In many real‑world scenarios, an assistant must retrieve structured information (e.g., product catalogs), execute business logic (e.g., calculate shipping costs), or personalize responses based on user context. The MCP server acts as a bridge, offering a standardized contract that the assistant can rely on without hard‑coding vendor APIs. Developers gain an abstract layer where they can swap underlying data stores or business logic without impacting the assistant’s integration.

Key capabilities of the demo include:

• Resource discovery – Clients can request a list of available data entities or metadata, enabling dynamic UI generation or context awareness.
• Tool execution – The server exposes executable tools (e.g., calculations, data transformations) that the assistant can invoke with typed arguments and receive structured results.
• Prompt management – Pre‑defined prompts are served on demand, allowing the assistant to tailor its language model prompts according to the current task or user profile.
• Sampling control – Parameters such as temperature, max tokens, and top‑p can be adjusted on the fly, giving developers fine‑grained control over the assistant’s output style.

In practice, this setup is valuable for e‑commerce platforms that need an AI assistant to browse products, apply discounts, or answer policy questions. It is equally useful for internal knowledge bases where the assistant must pull from an enterprise database, run compliance checks, or trigger workflow actions. By encapsulating these functions behind the MCP interface, teams can iterate on business logic independently of the AI model, ensuring consistency and reducing integration friction.

The demo’s Spring Boot foundation means it can be easily extended: adding new tools, integrating with GraphQL or gRPC services, or scaling horizontally behind a Kubernetes cluster. Its clear separation of concerns—controllers for protocol adherence, services for business logic, and configuration for sampling—provides a clean template that developers familiar with MCP can adapt to their own infrastructure. This makes the demo not just a proof of concept but a reusable scaffold for building robust, AI‑enabled applications.