Model Context Protocol (MCP) enables AI models to interact with external tools and services for reproducibility, predictable performance, and security guarantees.
This article demonstrates creating an MCP server in Java using Stockfish, a popular open-source chess engine.
Java was chosen for its robust ecosystem and widespread adoption in enterprise applications.
Implementing the MCP server involves setup with Stockfish binary in a Docker image and using the Quarkus application.
The server functionality with Quarkus involves dependencies, tool implementations, and integrating Stockfish analysis.
Building the MCP server involves creating a Docker container and using a multi-stage build for the application.
To test the server, the MCP inspector can be used to verify the functionality of the implemented tools.
Integration with AI assistants and potential configurations for using the Stockfish MCP server are discussed.
Creating an MCP server in Java is straightforward and beneficial for enhancing AI assistants with predictable functionality.
Using Docker for running MCP servers provides isolation, reproducibility, and security benefits.