giuseppe-trisciuoglio/developer-kit

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Migrating from LangChain4j MCP to Spring AI MCP

Name: giuseppe-trisciuoglio/developer-kit
Rating: 89.68656716417911 (1 reviews)
Author: giuseppe-trisciuoglio

Guide for migrating existing LangChain4j MCP server implementations to Spring AI.

Key Differences

Aspect	LangChain4j	Spring AI
Tool annotation	`@ToolMethod`	`@Tool(description)`
Parameter annotation	`@P`	`@ToolParam`
Configuration	Custom properties	Spring Boot auto-configuration
Security	Manual	Spring Security integration
Integration	Standalone	Deep Spring ecosystem integration
Function calling	`AiTool`	`FunctionCallback`

Migration Steps

Replace @ToolMethod("description") with @Tool(description = "description") on tool methods
Replace @P("description") on parameters with @ToolParam("description")
Wrap tool classes with @Component (LangChain4j may use different registration)
Update configuration from LangChain4j properties to spring.ai.mcp.* properties
Migrate prompt templates to use @PromptTemplate and @PromptParam
Replace LangChain4j-specific types with Spring AI equivalents
Add @EnableMcpServer to the main application class
Configure Spring Security if you had custom auth in LangChain4j

Code Migration Example

Before (LangChain4j):

public class WeatherTools {

    @ToolMethod("Get weather information for a city")
    public String getWeather(@P("city name") String city) {
        return weatherService.getWeather(city);
    }

    @ToolMethod("Get 5-day forecast")
    public String getForecast(
            @P("city name") String city,
            @P("temperature unit") String unit) {
        return weatherService.getForecast(city, unit);
    }
}

After (Spring AI):

@Component
public class WeatherTools {

    private final WeatherService weatherService;

    public WeatherTools(WeatherService weatherService) {
        this.weatherService = weatherService;
    }

    @Tool(description = "Get weather information for a city")
    public WeatherResponse getWeather(@ToolParam("City name") String city) {
        return weatherService.getWeather(city);
    }

    @Tool(description = "Get 5-day forecast")
    public ForecastResponse getForecast(
            @ToolParam("City name") String city,
            @ToolParam(value = "Temperature unit: celsius or fahrenheit", required = false) String unit) {
        return weatherService.getForecast(city, unit != null ? unit : "celsius");
    }
}

Configuration Migration

Before (LangChain4j application.properties):

langchain4j.mcp.enabled=true
langchain4j.mcp.transport=stdio
langchain4j.openai.api-key=${OPENAI_API_KEY}

After (Spring AI application.properties):

spring.ai.mcp.enabled=true
spring.ai.mcp.transport.type=stdio
spring.ai.openai.api-key=${OPENAI_API_KEY}
spring.ai.openai.chat.options.model=gpt-4o-mini

Prompt Template Migration

Before (LangChain4j):

public class CodePrompts {

    public AiPrompt createCodeReviewPrompt(String code) {
        return AiPrompt.builder()
                .system("You are a code reviewer.")
                .user("Review: " + code)
                .build();
    }
}

After (Spring AI):

@Component
public class CodePrompts {

    @PromptTemplate(
        name = "code-review",
        description = "Review Java code for best practices"
    )
    public Prompt createCodeReviewPrompt(@PromptParam("code") String code) {
        return Prompt.builder()
                .system("You are a code reviewer.")
                .user("Review the following code:\n```java\n" + code + "\n```")
                .build();
    }
}

Main Application Class Update

Before:

@SpringBootApplication
public class MyApplication {
    public static void main(String[] args) {
        SpringApplication.run(MyApplication.class, args);
    }
}

After:

@SpringBootApplication
@EnableMcpServer
public class MyApplication {
    public static void main(String[] args) {
        SpringApplication.run(MyApplication.class, args);
    }
}