Spring Ai Mcp Server Demo

The Spring AI MCP server demo is a lightweight, production‑ready implementation that exposes book‑review data through the Model Context Protocol. By publishing a small set of curated reviews, it demonstrates how an MCP server can act as a data source that AI assistants such as Claude or OpenAI’s agents can query in real time. This is particularly useful for developers who want to prototype or integrate external knowledge bases into conversational agents without building a full‑blown database backend.

At its core, the server implements a standard MCP endpoint that returns a list of book reviews in JSON format. Each review contains a title, author, rating, and a brief commentary. The server’s simplicity allows developers to focus on the MCP contract—defining resources, tools, and prompts—while leaving the heavy lifting of data persistence to Spring’s built‑in support. Because it is a Spring Boot application, the demo can be run with a single Maven command or launched directly from an IDE, making it ideal for quick testing and demonstration purposes.

Key capabilities of this MCP server include:

• Resource exposure: The endpoint is registered as an MCP resource, allowing AI clients to retrieve the full list of reviews or filter by parameters such as author or rating.
• Tool integration: The server can be paired with MCP tools that let agents add or update reviews, showcasing how mutable state can be managed within an MCP workflow.
• Prompt templating: Built‑in prompt templates enable agents to ask for “top rated books” or “reviews by a specific author,” demonstrating how natural language queries can be mapped to underlying data calls.
• Sampling support: The server can return a subset of reviews based on a sampling strategy, which is useful for large datasets where only a representative slice is needed.

Typical use cases span from educational chatbots that recommend books to content‑generation pipelines where an AI writes reviews based on curated data. In a corporate setting, the server could serve as a knowledge base for internal documentation or compliance reviews, allowing assistants to fetch and cite authoritative sources on demand. The lightweight nature of the demo also makes it an excellent teaching tool for workshops on MCP, illustrating how a Spring application can be quickly turned into a compliant server.

By integrating seamlessly with existing AI workflows, the Spring AI MCP server demo provides developers a concrete example of how to expose domain data through MCP, enabling richer, context‑aware interactions in AI assistants without reinventing the wheel.