Overview of the MCP Java SDK Examples Server

The MCP Java SDK Examples server is a curated showcase of how the Model Context Protocol (MCP) can be implemented in Java to expose data, tools, and prompts to large‑language models (LLMs). By providing a ready‑to‑run Java implementation, it solves the common pain point of bridging an LLM with external systems in a secure, standardized way. Developers who need to integrate file operations, database queries, or custom business logic into a conversational AI workflow can use these examples as a reference or starting point, eliminating the need to build an MCP server from scratch.

At its core, the server demonstrates four fundamental MCP concepts: Resources, Tools, Prompts, and Sampling. Resources act like read‑only endpoints that load contextual data into the LLM; for instance, a filesystem resource might expose metadata about files in a protected directory. Tools are write‑capable endpoints that perform actions—such as creating, updating, or deleting files—while ensuring proper access control. Prompts provide reusable templates that standardize how the LLM interacts with these resources and tools, enabling consistent conversational patterns across different use cases. Sampling capabilities allow the server to manage how the LLM generates responses, offering fine‑grained control over output length, temperature, and other generation parameters.

The examples are built on three Java SDKs: a lightweight Declarative MCP Java SDK, the Official MCP Java SDK, and the Spring AI MCP SDK. This diversity illustrates how the same MCP concepts can be applied across different Java ecosystems, from plain JDK applications to Spring Boot services. Each SDK brings its own flavor—declarative for quick prototyping, official for full feature parity, and Spring AI for seamless integration with the broader Spring ecosystem.

Real‑world scenarios that benefit from this server include:

• Secure file management: Exposing a controlled filesystem API to an LLM, allowing the assistant to read and modify files while respecting ACLs.
• Dynamic data fetching: Loading up‑to‑date information from databases or external APIs into the LLM’s context without exposing raw endpoints.
• Automated workflows: Triggering business processes (e.g., order fulfillment, ticket creation) through LLM‑initiated tools that perform side effects.
• Custom prompt libraries: Sharing reusable conversation templates across teams, ensuring consistent tone and structure.

Integrating the server into an AI workflow is straightforward: a client (e.g., Claude or any LLM that supports MCP) connects to the server, retrieves resources to build context, invokes tools when a user request requires action, and utilizes prompts to guide the LLM’s responses. Because MCP is standardized, developers can swap out or extend individual components without breaking the overall contract, enabling rapid iteration and experimentation.

In summary, the MCP Java SDK Examples server provides a practical, well‑documented foundation for developers to understand and leverage the full power of MCP in Java. It highlights how resources, tools, prompts, and sampling can be combined to create secure, extensible AI applications that interact seamlessly with external data sources and services.