server2mcp

Server2MCP

A huge, huge, huge thank you for your stars! This is a project I put an enormous amount of effort into. Though it’s initiated by me personally, I firmly believe the project boasts an excellent architecture and great practical value. Currently, it aligns with the June 18, 2025 version; once a new version is released, all these features will be updated uniformly.

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This is definitely a revolutionary idea!!!

This is a Spring Boot Starter used for automated integration of MCP (Model Control Protocol) services.

✨function characteristics

• Automatic configuration of MCP service, similar to the relationship between mybatis plus and mybatis, non-invasive, purely enhanced
• Supports all native functions of MCP in JavaSdk, providing tool registration and callback mechanisms, etc
• Support custom tool registration through @ToolScan annotation
• Supports custom resource registration through the @ResourceScan annotation
• Supports custom prompt registration through the @PromptScan annotation
• Users can customize the Parser without relying on chain of responsibility implementation to complete attribute parsing for unique interface annotations
• Having Javadoc, Swagger2, Swagger3, SpringMVC (only responsible for partial parsing logic), Jackson, and Springai native tool parsers

🌟 Latest features

• Benchmarking the latest MCP SDK version 0.11 snapshot version, customize annotations to experience the latest features, McpTool
• McpTool completely isolates Springai's Tool environment, implementing a tool system unique to Mcp. The method is automatically injected into Exchange, making it easy to complete MCP client interaction
• The configuration class mineType for springai is currently not supported and is integrated into the annotation properties of McpTool

👀Unfinished points

There are still many parsing extension points of Springai that have not been integrated. For example, currently only Javadoc version annotations have been completed, but the parsing architecture uses responsibility chains and templates, which are extremely easy to extend. In the future, various mainstream methods will be integrated to describe annotation parsing

🎯Quick start

Since it has not been pushed to the central repository yet, you can download the source code, In the folder 'server 2mcp starter webmvc' perform an mvn clean install, and then make dependency references

Add Dependency

<dependency>
    <groupId>com.ai.plug</groupId>
    <artifactId>server2mcp-starter-webmvc</artifactId>
    <version>1.1.0-SNAPSHOT</version>
</dependency>

Then add the configuration in the configuration file:

plugin:
  mcp:
    enabled: true
    parser:
      params: JAVADOC, TOOL, SpringMVC, JACKSON, SWAGGER2, SWAGGER3 # optional, default registration for parsers other than JAVADOC
      des: JAVADOC, TOOL, JACKSON, SWAGGER3, SWAGGER2 # optional, default registration for parsers other than JAVADOC
    scope: interface # There are two configurations, custom and interface. The default interface is pre registered as a tool under the controller; Custom does not pre register tools

The above is the basic configuration for starting the project, which includes all native configurations such as spring.ai.mcp.server-side, etc. The interface configuration will register all controllers under your startup class path as MCP interfaces by default. If there is an @ Depreciated annotation on the interface method or class, it will not be registered.

NOTICE：JAVADOC Parser

The parsing logic of Javadoc is essentially to parse source code files, and after going live, Java code exists in the form of bytecode class files, so Javadoc cannot be used. However, Javadoc's annotation method is still quite popular among developers, so it cannot be completely abandoned. Here is a solution. To use a Javadoc parser, you must package the source code into a resource directory. If you use Maven, you need to add a packaging configuration as follows:

            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-resources-plugin</artifactId>
                <executions>
                    <execution>
                        <id>copy-java-sources</id>
                        <phase>prepare-package</phase>
                        <goals>
                            <goal>copy-resources</goal>
                        </goals>
                        <configuration>
                            <outputDirectory>${project.build.outputDirectory}</outputDirectory>
                            <resources>
                                <resource>
                                    <directory>src/main/java</directory>
                                    <includes>
                                        <include>**/*.java</include>
                                    </includes>
                                </resource>
                            </resources>
                        </configuration>
                    </execution>
                </executions>
            </plugin>

📚principle

It can be understood as opening up interfaces to AI, so these interfaces are the same as regular interfaces, except that they can be called through AI. Relevant knowledge documents:Model Context Protocol (MCP) :: Spring AI Reference , [Introduction - Model Context Protocol](https://docs.spring.io/spring-ai/reference/api/mcp/mcp-overview.html）And [Introduction - Model Context Protocol]（https://modelcontextprotocol.io/introduction)

💕Best practices

With this framework, you no longer need to rebuild an MCP service application from scratch, nor do you need to add @ Tool annotations to highly coupled copied code, or add MCP functionality to source code. You only need to add custom @ ToolScan annotations based on a configuration class to easily complete the registration of MCP interfaces. What should you do if you encounter MCP SDK revisions? Don't worry, the core content is maintained by me, and the usage method remains unchanged

1. You can easily build a multi-agent application by using multiple AI dialogue interfaces that you have customized, and then simply calling the corresponding MCP interface on the client side.

2. It can quickly access AI dialogue calls for your management system, with high customization. You don't need to pay attention to any details in the AI field, just focus on your favorite areas of web and front-end, and you can achieve cool effects. Compared to this

3. It can be used in conjunction with simple MCP clients like cursor to easily complete interface debugging

🔔summarize

This framework is actually very simple, and there may be many vulnerabilities and shortcomings in the code. Please forgive me.

📄 Copyright Statement/Open Source Agreement

According to the Apache 2.0 license Published code