OpenMeteo MCP Server

Overview of the MCP Java Sample Server

The MCP Java Sample is a lightweight Spring Boot application that demonstrates how to expose an MCP (Model Context Protocol) server using Spring AI’s web MVC starter. It solves the common developer pain point of quickly provisioning a fully‑functional MCP endpoint without needing to write boilerplate server code. By packaging the implementation into a single executable JAR, teams can integrate an external MCP server into their AI toolchains—be it Cursor, Claude Desktop, VS Code, or custom clients—with minimal friction.

At its core, the server listens for MCP requests over HTTP, interprets them as model context operations, and delegates to underlying AI services. This separation allows the client to remain agnostic of the model provider, while still benefiting from advanced features such as resource management, tool invocation, and prompt orchestration. The server’s modular design means developers can replace or extend the underlying AI backend (e.g., switching from OpenAI to Anthropic) without touching client code.

Key capabilities include:

• Resource provisioning: Clients can request and manage model resources, ensuring consistent state across sessions.
• Tool integration: The server can expose custom tools—such as database queries or external API calls—that the AI model can invoke, enabling richer, context‑aware interactions.
• Prompt and sampling control: Fine‑grained prompt construction and sampling parameters are exposed, allowing developers to tailor the model’s behavior for specific use cases.
• Spring Boot ergonomics: Leveraging Spring AI’s starter simplifies dependency injection, configuration, and health monitoring, giving developers a familiar development experience.

Typical use cases span from rapid prototyping of AI‑powered assistants to production deployments where the MCP server acts as a secure gateway between internal data sources and external language models. For instance, an enterprise can host the server behind its firewall, expose only vetted tools to the AI, and enforce compliance policies—all while clients continue to use their preferred MCP‑compatible interfaces.

What sets this sample apart is its minimalism coupled with full compatibility. It provides a ready‑to‑run reference implementation that can be dropped into existing Java ecosystems, tested locally, and then scaled to Kubernetes or cloud‑native environments. Developers gain the advantage of a proven MCP contract, built on Spring AI’s robust abstractions, enabling rapid iteration and confident integration into complex AI workflows.