Spring MCP Server

Overview

The Spring MCP Server is a lightweight, Java‑based implementation of the Model Context Protocol (MCP) that leverages Spring‑AI to expose AI‑ready tools and resources over a simple HTTP interface. It solves the problem of connecting conversational agents—such as Claude or other LLMs—to domain‑specific services without requiring custom integrations for each provider. By adhering to MCP, the server presents a standardized API that any compliant AI client can discover and invoke, enabling rapid prototyping and deployment of tool‑augmented assistants.

At its core, the server hosts a single tool named . When an AI assistant receives a user request that involves booking a medical appointment, it can call this tool by supplying the hospital name as a parameter. The server processes the request and returns a structured response, such as confirmation details or available time slots. This pattern illustrates how MCP servers can encapsulate business logic (e.g., scheduling, data retrieval, or external API calls) behind a consistent protocol, allowing the assistant to focus on dialogue management while delegating domain tasks to reliable services.

Key features of the Spring MCP Server include:

• SSE (Server‑Sent Events) Transport: The server supports SSE for low‑latency, real‑time communication. Clients can subscribe to a persistent stream () and receive tool invocation responses as they become available.
• Tool Discovery: Clients can inspect the endpoint to list all available tools. In this demo, only is exposed, but the framework scales to many more.
• Resource and Prompt Exposure: While this minimal example focuses on tools, the underlying Spring‑AI integration can expose additional resources (e.g., datasets) and prompts that AI assistants can leverage.
• Inspector Compatibility: The included Node.js inspector () allows developers to connect, view available tools, and test invocations directly from the browser, simplifying debugging and validation.

Typical use cases for this server are:

• Healthcare Assistants: Automating appointment booking, checking doctor availability, or retrieving patient records through secure, standardized calls.
• Enterprise Chatbots: Integrating internal scheduling or resource‑allocation services into conversational agents that serve employees.
• Rapid Prototyping: Quickly turning a Spring application into an AI‑accessible service without writing custom adapters for each LLM platform.

By exposing domain logic through MCP, developers can decouple AI workflow design from backend implementation. The assistant remains agnostic to the underlying technology stack, while the server guarantees a consistent interface for tool invocation. This modularity accelerates development cycles, promotes reuse of existing services, and ensures that AI agents can reliably perform complex tasks across diverse environments.