solon-ai-mcp-embedded-examples
LocalAbout solon-ai-mcp-embedded-examples
solon-ai-mcp-embedded-examples is a collection of Java examples for Solon AI and MCP integrations. It demonstrates AI features and MCP clients and servers embedded in frameworks including Solon, Spring Boot, jFinal, Vert.x, Quarkus, and Micronaut.
Key features
- 01Chat model usage and tests
- 02Tool calling examples and tests
- 03Skills and RAG examples
- 04MCP SSE and streamable examples
- 05Embedded client and server examples
Use cases
- 01Learning MCP client and server setup
- 02Trying AI integrations in supported Java frameworks
- 03Exploring RAG, skills, and tool calling examples
MCP support
The examples cover MCP clients and servers, including SSE and streamable examples. The README mentions support for MCP_2025_03_26.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEsolon-ai-embedded-examples
solon-ai & solon-ai-mcp & solon-ai-flow & solon-ai-agent 同时支持：java8、java11、java17、java21、java25（java8+）
本示例项目会经常更新，建议经常同步。
说明
示例内容包括：
- llm（需要修改 llm/_Constants 下的常量配置，否则启动会出错）
- chatModel 基本使用，及单测
- tool call 基本使用，及单测
- skills（solon ai skills）基本使用及单测
- rag 基本使用，及单测（embeddingModel，repository，splitter）
- mcp （含 sse, streamable 示例；支持 MCP_2025_03_26 协议）
- server 基本使用（需要 solon 容器支持）
- client 基本使用，及单测
示例
| 示例 | 说明 |
|---|---|
| solon-ai-in-jfinal | |
| solon-ai-in-jfinal-newstyle | |
| solon-ai-in-quarkus | |
| solon-ai-in-solon | |
| solon-ai-in-springboot2 | |
| solon-ai-in-springboot3 | |
| solon-ai-in-springboot4 | |
| solon-ai-in-springmvc | |
| solon-ai-in-vertx | |
| solon-ai-in-webflux2-llm | 只有 llm 示例（没有 mcp） |
| solon-ai-in-webflux3-llm | 只有 llm 示例（没有 mcp） |
| solon-ai-in-webflux4-llm | 只有 llm 示例（没有 mcp） |
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.