Overview

The Solon AI MCP Embedded Examples project delivers a ready‑to‑run suite of demonstrations that showcase how the Model Context Protocol (MCP) can be integrated into modern Java applications. By bundling a lightweight MCP server, client libraries, and practical examples across multiple frameworks—Solon, Spring Boot, Vert.x, and JFinal—the repository gives developers a clear blueprint for embedding AI capabilities directly into their services.

This MCP server addresses the core challenge of bridging conversational AI with existing enterprise stacks: it exposes LLMs, tool‑calling logic, and retrieval‑augmented generation (RAG) through a standardized protocol while keeping the deployment footprint minimal. Instead of spinning up separate microservices or relying on external API gateways, developers can run the MCP server inside their familiar Solon container, eliminating latency and simplifying security management.

Key capabilities highlighted in the examples include:

• LLM integration with basic chat, tool‑calling, and RAG workflows. Each scenario demonstrates how to configure embedding models, repositories, and text splitters without writing boilerplate code.
• MCP server and client that support Server‑Sent Events (SSE) and streamable responses, fully compliant with the MCP_2025_03_26 specification. The server runs on a Solon container, while the client library offers straightforward invocation patterns and unit‑testable interactions.
• Framework‑agnostic examples that adapt the MCP client to Solon MVC, Spring Boot 2/3, Vert.x, and JFinal. This illustrates how to embed AI assistants into both synchronous and asynchronous request pipelines.

Typical use cases span from building conversational chatbots that can execute external tools (e.g., database queries, API calls) to constructing knowledge‑base assistants that retrieve and summarize documents on demand. Because the MCP server can stream partial responses, developers can implement real‑time feedback loops or progressive rendering in UI clients. Moreover, the embedded nature of the server means it can run on edge devices or within Kubernetes pods without additional networking overhead.

In summary, the Solon AI MCP Embedded Examples provide a comprehensive, hands‑on reference for developers who need to embed robust AI assistants into their Java applications. By demonstrating end‑to‑end workflows—from LLM configuration to MCP protocol handling—this project reduces integration friction and accelerates time‑to‑value for AI‑powered services.