Spring MCP Server Demo

Overview

The Spring MCP Server Demo is a lightweight, production‑ready implementation of the Model Context Protocol (MCP) written in Java and powered by Spring Boot. It demonstrates how an AI assistant can discover, call, and receive results from external services without embedding those capabilities directly into the model. By exposing tools as RESTful endpoints that conform to MCP’s specification, developers can plug custom logic—such as knowledge‑base queries or external API calls—into the assistant’s reasoning loop.

At its core, the server registers a single “search” tool that performs Retrieval‑Augmented Generation (RAG) against a local documentation repository. When an AI model receives a prompt that requires up‑to‑date or domain‑specific information, it can invoke this tool via MCP’s action. The server receives the query, executes a text‑search against the RAG directory, and streams back relevant snippets. This pattern keeps the model lightweight while giving it access to a persistent knowledge source, enabling more accurate and context‑aware responses.

Key features include:

• MCP compliance: Implements the full MCP protocol stack, allowing any compliant AI client to discover tools, view metadata, and invoke them seamlessly.
• Spring AI integration: Leverages Spring’s dependency injection, configuration management, and actuator endpoints for monitoring and health checks.
• Docker support: A simple script builds a container image that can be deployed on any OCI‑compatible runtime, simplifying CI/CD pipelines.
• Extensibility: New tools can be added by implementing a bean and registering it in the MCP registry, following the same pattern as the RAG search.

Typical use cases involve building knowledge‑intensive assistants that must reference internal documentation, codebases, or proprietary APIs. For example, a customer support bot can query product manuals via the search tool; an engineering assistant can retrieve code snippets from a repository; or a compliance bot can pull policy documents on demand. In each scenario, the MCP server acts as a bridge between the AI model’s request and domain‑specific data, keeping the model stateless while enriching its output.

Because MCP is language‑agnostic, this Java server can be paired with any LLM that supports the protocol—Claude, GPT‑4o, or custom models. Developers can iterate rapidly: update the search index, add new tools, or expose external APIs, all without retraining the model. The result is a modular, maintainable architecture that scales with business needs while keeping AI workflows simple and transparent.