1. Introduction

It’s hard to imagine doing web application automation without needing to click on anything or submit a form. Although these actions appear to be simple, there are nuances to be aware of.

In this tutorial, we’ll look into how Selenium handles click() and submit(), and which one we should choose when working with forms.

2. Prerequisites and Setup

First, let’s set up a simple Selenium test.

To that end, we assume basic familiarity with several concepts:

• Java development environment
• initializing a Maven project
• running tests

For the example code, we use a quickstart Maven project and add Selenium WebDriver, WebDriver Manager, and JUnit packages to the pom.xml file:

<dependencies>
  <dependency>
    <groupId>org.seleniumhq.selenium</groupId>
    <artifactId>selenium-java</artifactId>
    <version>4.46.0</version>
  </dependency>
  <dependency>
      <groupId>io.github.bonigarcia</groupId>
      <artifactId>webdrivermanager</artifactId>
      <version>6.3.0</version>
  </dependency>
  <dependency>
    <groupId>org.junit.jupiter</groupId>
    <artifactId>junit-jupiter</artifactId>
    <version>5.11</version>
    <scope>test</scope>
  </dependency>
</dependencies>

Let’s leverage WebDriver Manager to automatically download and set up the correct version of the browser driver we use in the tests. Furthermore, we employ JUnit to annotate and organize the sample code.

Next, we create a sample test file in the /test/java directory of the project. Let’s name it ContactTest.java and add setup code to get started:

public class ContactTest {
    private WebDriver driver;
    @BeforeEach
    void setup() {
        WebDriverManager.chromedriver().setup();
        driver = new ChromeDriver();
    }
    @AfterEach
    void teardown() {
        if (driver != null) {
            driver.quit();
        }
    }
    // add tests here
}

Here in the setup and teardown functions, we initialize the Chrome driver before and quit the driver after each test.

3. How Selenium Works

Selenium WebDriver executes commands by forwarding them to the respective browser driver (e.g., chromedriver for Chrome, or geckodriver for Firefox). In essence, it’s a messenger sending HTTP requests.

On the other hand, browsers guard sensitive actions, also known as activation behavior, like click() and submit(). The goal is, for example, to prevent a malicious script from triggering a click multiple times in an attempt to exploit user actions. This security measure is implemented with the isTrusted flag. All events have this flag to differentiate between human and script interactions. Events dispatched directly by JavaScript (such as those via dispatchEvent()) have isTrusted set to false, whereas WebDriver user interaction commands such as click() generate trusted browser events.

Since Selenium uses the WebDriver protocol, it has elevated privileges, including triggering a click multiple times without getting blocked. Essentially, events Selenium triggers have their isTrusted flag set to true when the browser ystem processes them.

4. Exploring click()

Just like when operating manually, we can call click() on any clickable element.

4.1. Implementation Details

By design, it mimics human behavior. Therefore, the element needs to be enabled, visible, and have a non-zero size for a click to work.

When Selenium sends the click message, the browser driver executes a series of actions:

1. ensures the element is clickable
2. scrolls to make the element visible
3. calculates the element’s central coordinates for dispatching the events
4. dispatches mouse down and mouse up events
5. dispatches a click event

Let’s look at the click() implementation in the Selenium WebDriver code:

@Override
public void click() {
  execute(DriverCommand.CLICK_ELEMENT(id));
}

According to the documentation, click() can throw several exceptions:

• NoSuchElementException when the element is missing
• ElementNotInteractableException when the element is hidden, disabled, or has zero size
• ElementClickInterceptedException when another element obscures the target
• StaleElementReferenceException when a reference is no longer valid

For a more controlled click, we can use the Selenium Actions API:

new Actions(driver).moveToElement(submitButton).click().perform();

Nonetheless, unlike the regular click(), it doesn’t throw an exception when another element obscures the target element. Notably, Actions always performs a click at the specified coordinates regardless of whether the click ends up on a different element.

4.2. Example

To see click() in action, we implement and run a simple test that fills a form, then clicks the submit button:

@Test
void submitFormWithClick() throws InterruptedException {
    try {
        WebDriverWait wait = new WebDriverWait(driver, Duration.ofSeconds(10));
        driver.get("https://www.baeldung.com/contact");
        WebElement nameField = wait.until(
            ExpectedConditions.presenceOfElementLocated(By.name("your-name"))
        );
        nameField.sendKeys("Jane Doe");
        TimeUnit.SECONDS.sleep(1);
        WebElement emailField = driver.findElement(By.name("your-email"));
        emailField.sendKeys("janedoe@mail.com");
        TimeUnit.SECONDS.sleep(1);
        WebElement messageField = driver.findElement(By.name("your-message"));
        messageField.sendKeys("Hello, this is an automated test message.");
        TimeUnit.SECONDS.sleep(1);
        WebElement submitButton = wait.until(
          ExpectedConditions.elementToBeClickable(By.cssSelector("input[type='submit']"))
        ); 
        submitButton.click();
        TimeUnit.SECONDS.sleep(2);
    } catch (Exception e) {
        e.printStackTrace();
    }
}

In this case, we use TimeUnit.SECONDS.sleep(1) to follow along the test execution. After filling the form, the page scrolls down to show the submit button before clicking it:

Unfortunately, the submit button doesn’t have a click animation to demonstrate UI effects.

5. Exploring submit()

As expected, submit() is more limited than click() as its only purpose is to submit a form.

5.1. Implementation Details

We can call submit() on the form element to trigger the form submit functionality. In addition, we can call submit() on any element within the form. In the latter case, the parent form element is found automatically, and Selenium calls submit on the form.

When no form exists, the function should throw a NoSuchElementException. In reality, it often throws a different exception — UnsupportedOperationException. Why that happens is fairly obvious when we look at the submit() implementation:

@Override
public void submit() {
  try {
    execute(DriverCommand.SUBMIT_ELEMENT(id));
  } catch (JavascriptException ex) {
    String message = "To submit an element, it must be nested inside a form element";
    throw new UnsupportedOperationException(message);
  }
}

The code looks similar to the click() code. However, when calling submit(), there’s no visual feedback, since we’re directly executing submit on the form. No submit or button click events trigger. As a result, any additional functionality relying on those event handlers is completely bypassed.

Furthermore, the submit button or another form element on which we called submit() doesn’t need to be visible or clickable.

5.2. Example

Let’s add another test by copying the previous test, renaming it, changing click() to submit(), and running the result:

@Test
void submitFormWithSubmit() throws InterruptedException {
    try {
        WebDriverWait wait = new WebDriverWait(driver, Duration.ofSeconds(10));
        driver.get("https://www.baeldung.com/contact");
        WebElement nameField = wait.until(
            ExpectedConditions.presenceOfElementLocated(By.name("your-name"))
        );
        nameField.sendKeys("Jane Doe");
        TimeUnit.SECONDS.sleep(1);
        WebElement emailField = driver.findElement(By.name("your-email"));
        emailField.sendKeys("janedoe@mail.com");
        TimeUnit.SECONDS.sleep(1);
        WebElement messageField = driver.findElement(By.name("your-message"));
        messageField.sendKeys("Hello, this is an automated test message.");
        TimeUnit.SECONDS.sleep(1);
        WebElement submitButton = wait.until(
            ExpectedConditions.elementToBeClickable(By.cssSelector("input[type='submit']"))
        ); 
        submitButton.submit();
        TimeUnit.SECONDS.sleep(2);
    } catch (Exception e) {
        e.printStackTrace();
    }
}

As expected, the page didn’t scroll to show the submit button:

In essence, the submit() behavior doesn’t mimic normal user interaction at all. The end result is the same as if we called click() on the submit button. However, the interaction is purely programmatic.

6. submit() Deprecation

In modern Selenium (version 4 and later), submit() is discouraged, although it’s not yet obsolete and the code doesn’t flag it as deprecated. In fact, the official Selenium documentation advises against using submit(). The reason is the lack of support by browser drivers.

Earlier versions of Selenium used a dedicated endpoint for this. However, starting from Selenium 3, browser vendors handle the driver implementations. Some immediately started to strictly adhere to the new official W3C WebDriver Protocol, which has no submit() specification. That broke backward compatibility, forcing Selenium to implement a workaround.

Currently, instead of the old submit message, submit() sends a message to a different endpoint to execute a custom script. Because the browser drivers don’t handle this code, the event has the isTrusted flag set to false.

7. click() vs submit()

Obviously, since submit() is no longer recommended, we should use click() to submit forms. However, let’s ignore this fact for a moment and analyze the reasons why click() is a better choice.

At the core of the Selenium philosophy, and UI automation in general, lies the goal to automate what a real user would do as closely as possible. If a real user won’t be able to interact with the element to submit the form, then a passing test that programmatically achieves submitting hides a potential usability issue. On the other hand, click() achieves the core goal perfectly.

Moreover, modern web applications don’t always follow a traditional form structure and have a root <form> element. Unlike submit(), click() is more flexible and works with any UI structure.

Lastly, the security advantage. Because click() action has its isTrusted flag set to true, it’s reliable, correctly propagates a linked series of events, and runs no risk of getting flagged by security tools.

8. Conclusion

In this article, we looked at how Selenium operates under the hood. Also, we saw how click() and submit() work, and why we should use click().

Although there’s practically no scenario where we would want to use it with modern Selenium, submit() serves an educational goal of highlighting important nuances in automation implementation.

       

1. Spring and Java

>> The Archaeologist’s Copilot [martinfowler.com]

Legacy systems don’t really come mapped. Not usually at least.

This writeup shows how a coding assistant can help investigate an unfamiliar codebase, reconstruct its architecture, and turn scattered evidence into useful documentation. A developer is a central part of this work.

Also worth reading:

• >> Making ServiceLoader usable: a provider factory [frankel.ch]
• >> Your Loom App Quietly Became a Thread Pool Again: A Field Guide to Virtual Thread Pinning [foojay.io]
• >> I Asked GitHub Copilot to Profile a Java App. It Found a Bug in My Heap Sizing, and Offered to Fix It [foojay.io]
• >> Guardrails with OPA Policies in Quarkus LangChain4j [quarkus.io]
• >> Temporal Is to Your Code What a Database Is to Your Data [foojay.io]
• >> When NOT TO USE Event-Driven Architecture (EDA) [foojay.io]
• >> “This Can’t Possibly Work”: What I Learned at a Temporal.io Workshop [foojay.io]

Webinars and presentations:

• >> Foojay Podcast #100: When a Podcaster Interviews Podcasters, and What They All Have in Common [foojay.io]
• >> Valhalla, Now! [nipafx.dev]
• >> Quarkus Insights #254: Domain-Driven Design and Hexagonal Architecture – Part 2 [quarkus.io]
• >> Spring Office Hours Podcast: S5E18 – The Latest from OpenAI, Anthropic and Spring AI 2.0 [spring.io]

Time to upgrade:

• >> BoxLang 1.15.0 Released: Blazing Fast Strings, Runtime Portability, and much more [foojay.io]
• >> Quarkus 3.37.3 – Maintenance release [quarkus.io]
• >> JHipster v9.2.0 [github.com/jhipster]
• >> Vert.x 5.1.5 and 4.5.30 [github.com/eclipse-vertx]
• >> Grails 7.2.1, 7.1.4, and 7.0.14 [github.com/apache]
• >> Micronaut Core 5.0.6, 5.1.6, and 3.10.10 [github.com/micronaut-projects]
• >> Piranha CMS Version 12.2 and Version 12.1 [github.com/PiranhaCMS]

2. Technical & Musings

>> Building Service Topology at Scale: Architecture, Challenges, and Lessons Learned [netflixtechblog.com]

This deep dive explains how Netflix builds a service-topology platform from multiple data sources, resolves conflicting signals, and turns the resulting graph into practical infrastructure. Interesting weekend read.

Also worth reading:

• >> Which Doc Format is Best for AI Specifications? [vanillajava.blog]
• >> The Tower Keeps Rising [pocoo.org]
• >> Okta SAML and Keycloak for ID-JAG Cross App Access [christianposta.com]
• >> DSLs Enable Reliable Use of LLMs [martinfowler.com]
• >> Choosing the right tool safety approach for coding agents [scottlogic.com]
• >> Measuring the Impact of Personalized Recommendations [medium.com]
• >> Adopting Agentic: Talks and Reflections from Our AI Engineering Event [scottlogic.com]

3. Pick of the Week

We’re now reaching the end of our Summer Sale:

>> Summer Sale – 30% Off in All Access

If you’re planning to join, now is a good time to get into the platform and start exploring.

       

1. Introduction

When we map entity relationships with Hibernate, a small mistake in a field declaration can prevent our application from booting up. A common example is the “org.hibernate.AnnotationException: Illegal attempt to map a non collection as a @OneToMany, @ManyToMany or @CollectionOfElements” error.

In this tutorial, we’ll look at why Hibernate throws this exception. Next, we’ll reproduce it with a simple mapping and walk through a couple of ways to fix it.

2. Understanding the Exception

Hibernate expects any field annotated with @OneToMany, @ManyToMany or @ElementCollection to be a collection-valued association. In practice, this means we must declare the field using one of the collection interfaces it recognizes:

• Collection or List: an ordered group that allows duplicate elements
• Set: a group that contains only unique elements
• Map: a set of key-value pairs, useful for associations based on keys

The reason for this requirement is that Hibernate manages these collections with its own implementation. When we persist an entity, Hibernate replaces that field value with a proxy that supports lazy loading and dirty checking. It can only do that if the field type is an interface it controls.

As such, when the annotation is used on a field that isn’t one of these interfaces, Hibernate can’t build that proxy. Therefore, it fails fast during startup and throws the AnnotationException.

3. Reproducing the Exception

Let’s reproduce the problem with a classic parent-child mapping. First, let’s define a Comment entity:

@Entity
public class Comment {
    @Id
    @GeneratedValue
    private Long id;
    private String text;
    // getters and setters
}

Now, let’s create a Post entity that owns many comments. Here, purposely declare the field with the concrete ArrayList type:

@Entity
public class Post {
    @Id
    @GeneratedValue
    private Long id;
    @OneToMany
    private ArrayList<Comment> comments = new ArrayList<>();
    // getters and setters
}

When Hibernate scans this mapping during building of the metadata at startup, it throws: org.hibernate.AnnotationException: Illegal attempt to map a non collection as a @OneToMany, @ManyToMany or @CollectionOfElements: com.baeldung.Post.comments

Note that newer Hibernate versions phrase the same message with @ElementCollection instead of the legacy @CollectionOfElements, but the cause is identical.

Even though ArrayList is technically a list, Hibernate rejects it because it’s a concrete class rather than a collection interface.

4. Fixing the Mapping

@Entity
public class Post {
    @Id
    @GeneratedValue
    private Long id;
    @OneToMany
    private List<Comment> comments = new ArrayList<>();
    // getters and setters
}

@Entity
public class Comment {
    @Id
    @GeneratedValue
    private Long id;
    @OneToMany
    private Post post;
}

@Entity
public class Comment {
    @Id
    @GeneratedValue
    private Long id;
    @ManyToOne
    private Post post;
}

       

1. Overview

Getting the current Java process ID helps with monitoring, logging, and system administration. Before Java 9, no standard API existed for this task. The platform has since filled this gap. Java 9 introduced the ProcessHandle API. Java 10 added getPid() to RuntimeMXBean. However, many older methods remain useful for specific scenarios.

In this tutorial, we’ll explore multiple ways to get the process ID of the current Java process.

2. The Pre-Java 9 Approach: Parsing the RuntimeMXBean Name

When using Java 8 or earlier, we can attempt to use ManagementFactory.getRuntimeMXBean().getName() to get the process ID. However, the string we get should be in the format PID@hostname. In this case, the PID is the numeric portion before the @ symbol.

So, let’s see how we can extract the PID from such a string:

import java.lang.management.ManagementFactory;
import java.lang.management.RuntimeMXBean;
public class ProcessIdUtil {
    
    public static String getProcessIdPreJava9() {
        String runtimeName = ManagementFactory.getRuntimeMXBean().getName();
        int index = runtimeName.indexOf('@');
        if (index < 1) {
            return null;
        }
        return runtimeName.substring(0, index);
    }
}

This approach has a significant limitation. According to the Java documentation, the returned name string can be any arbitrary string. The JVM implementation chooses what information to embed. While most JVMs include the PID in this format, we can’t guarantee it. This means the parsing approach may break on some JVM implementations or future versions.

3. Java 9+: ProcessHandle

Java 9 introduced the ProcessHandle interface as part of JEP 102 (Java Process API). It provides a direct, standardized way to access the native process ID. Moreover, we can access this interface from java.base with no extra dependencies.

The ProcessHandle.current() method returns a handle to the current process, and its pid() method returns the native process ID.

Let’s see this in action:

import java.lang.ProcessHandle;
    public static long getProcessIdJava9Plus() {
        return ProcessHandle.current().pid();
    }

The ProcessHandle API provides a type-safe long return value instead of a string and is part of the official Java standard library. This means we don’t need any external dependencies or vendor-specific hacks. Furthermore, the API is designed to be cross-platform.

4. Java 10+: RuntimeMXBean.getPid()

Later, Java 10 added a getPid() default method to the RuntimeMXBean interface. RuntimeMXBean.getPid() also returns the process ID directly as a long value without any string parsing.

Let’s see this in practice:

import java.lang.management.ManagementFactory;
import java.lang.management.RuntimeMXBean;
 
    public static long getProcessIdJava10Plus() {
        return ManagementFactory.getRuntimeMXBean().getPid();
    }

Here, we use one of the cleanest JMX-based approaches. This method is convenient because it uses the familiar ManagementFactory API. It’s also a default method, so it’s available on JVMs through the interface’s default implementation.

5. Creating a Version-Aware Utility

In practice, we can create a single utility method that works across different Java versions.

Let’s build a utility class that tries the most modern methods first and falls back to older ones as needed:

import java.lang.management.ManagementFactory;
import java.lang.management.RuntimeMXBean;
public class ProcessIdUtil {
    
    public static long getProcessId() {
        RuntimeMXBean runtimeMxBean = ManagementFactory.getRuntimeMXBean();
        
        if (isJavaVersionOrGreater(10)) {
            return runtimeMxBean.getPid();
        }
        
        if (isJavaVersionOrGreater(9)) {
            return ProcessHandle.current().pid();
        }
        
        String runtimeName = runtimeMxBean.getName();
        int index = runtimeName.indexOf('@');
        if (index > 0) {
            return Long.parseLong(runtimeName.substring(0, index));
        }
        
        throw new IllegalStateException("Cannot determine process ID");
    }
    
    private static boolean isJavaVersionOrGreater(int version) {
        String javaVersion = System.getProperty("java.version");
        if (javaVersion.startsWith("1.")) {
            javaVersion = javaVersion.substring(2);
        }
        int majorVersion = Integer.parseInt(javaVersion.split("\\.")[0]);
        return majorVersion >= version;
    }
}

In this code, we demonstrate version detection and graceful fallback. However, in modern Java development, we typically target a specific Java version. Put simply, the version-aware approach is most useful if we work with libraries that need to support multiple Java versions.

6. Conclusion

In this article, we explored different approaches to get the process ID in Java. Specifically, we saw how the  ManagementFactory.getRuntimeMXBean().getName() approach usually works for older Java versions, though it requires string parsing. Further, we also looked at the ProcessHandle.current().pid() method, which is a simple, type‑safe API in Java 9. Next, we tried RuntimeMXBean.getPid() from Java 10 – another convenient option. Finally, we created a version-aware way to provide a balance of compatibility and modern methods.

As always, all the source code is available over on GitHub.

       

1. Overview

In this tutorial, we’ll look at several ways to log HTTP request and response bodies using a Spring MVC HandlerInterceptor without disrupting normal request handling.

In short, we’ll use a servlet filter, in particular the ContentCaching wrapper, to log details of a simple REST API.

2. Spring MVC Execution Chain

First, we need to learn about the stages of a request within the Spring MVC execution chain.

The chain starts with servlet filters, followed by the DispatcherServlet, the handler execution chain, and finally the HTTP message conversion layer.

We’re interested in the HandlerInterceptors, which are part of the handler execution chain.

2.1. Understanding HandlerInterceptor

After the request reaches the DispatcherServlet, Spring needs to resolve a handler and run the handler execution chain, which may include zero or more HandlerInterceptors. The chain contains preHandle(), then the controller and its related logic, followed by postHandle() and afterCompletion().

The behavior of postHandle() depends on the controller’s return type. When the controller returns a view, the response is still uncommitted, so postHandle() can still modify it. With @ResponseBody or ResponseEntity, however, the HTTP message converters write and commit the body before postHandle() runs, so the response is effectively read-only at that point.

2.2. Request and Response Bodies Are One-Shot

A characteristic of the Request and Response bodies is that both are represented as streams. Thus, once any component consumes the stream, it cannot be read again unless we explicitly save it in some buffer.

This is important because if a filter or interceptor reads the body before it reaches Spring’s HTTP Message conversion layer (HttpMessageConverter), the converter receives an empty stream, and @RequestBody deserialization fails:

Now we have all the elements to understand the underlying details and how Spring handles HTTP requests.

3. Example REST API

Let’s create a simple REST API that creates books using @RequestBody:

public record CreateBookRequest(String title, String author) { }
public record BookCreatedResponse(UUID id, String title, String author) { }
@RestController
@RequestMapping("/api/books")
public class BookController {
    @PostMapping
    public ResponseEntity<BookCreatedResponse> create(@RequestBody CreateBookRequest request) {
        BookCreatedResponse response = new BookCreatedResponse(
            UUID.randomUUID(),
            request.title(),
            request.author()
        );
        return ResponseEntity.status(HttpStatus.CREATED).body(response);
    }
}

Now, the goal here is to log both the incoming CreateBookRequest and BookCreatedResponse records for every call with HTTP method, URI, and status.

4. ContentCaching Wrappers in a Filter

With Spring, we can use ContentCachingRequestWrapper and ContentCachingResponseWrapper to save the request and response content in a cache and expose it via getContentAsByteArray(). So even after we first read the stream, we don’t lose the content in subsequent layers.

To accomplish this, we’ll implement a servlet filter that gets registered to all requests by extending OncePerRequestFilter:

@Component
public class CachingHttpFilter extends OncePerRequestFilter {
    @Override
    protected void doFilterInternal(HttpServletRequest request, HttpServletResponse response,
      FilterChain filterChain) throws ServletException, IOException {
        ContentCachingRequestWrapper cachingRequest = new ContentCachingRequestWrapper(request);
        ContentCachingResponseWrapper cachingResponse = new ContentCachingResponseWrapper(response);
        try {
            filterChain.doFilter(cachingRequest, cachingResponse);
        } finally {
            cachingResponse.copyBodyToResponse();
        }
    }
}

Filters run before the DispatcherServlet, so we save everything before reaching the controller itself. Now let’s see how to use this cached value.

Notably, Spring already ships CommonsRequestLoggingFilter for request-side logging.

4.1. Logging Cached Bodies in HandlerInterceptor

To log the cached values, we implement HandlerInterceptor. Let’s build it step by step.

First, we declare the class and its logger:

@Component
public class HttpLoggingInterceptor implements HandlerInterceptor {
    private static final Logger log =
        LoggerFactory.getLogger(HttpLoggingInterceptor.class);
}

This is a Spring-managed component implementing HandlerInterceptor, so it can be registered later via WebMvcConfigurer.

Next, in preHandle(), we log only lightweight metadata — method and URI — and deliberately avoid touching the body stream, since the HttpMessageConverter hasn’t run yet and the cache isn’t populated:

@Override
public boolean preHandle(HttpServletRequest request, HttpServletResponse response,
  Object handler) {
    log.info("Incoming {} {}", request.getMethod(), request.getRequestURI());
    return true;
}

The right place to log the full request and response is afterCompletion(), because by that point, both the controller and the HttpMessageConverter have run, so the ContentCaching wrappers are fully populated:

@Override
public void afterCompletion(HttpServletRequest request, HttpServletResponse response,
  Object handler, Exception ex) {
    String requestBody = extractRequestBody(request);
    String responseBody = extractResponseBody(response);
    log.info("HTTP {} {} status={} requestBody={} responseBody={}", request.getMethod(),
      request.getRequestURI(), response.getStatus(), requestBody, responseBody);
}

Finally, the helper methods read the cached bytes and convert them to a string. Both extract methods guard with an instanceof check. If we haven’t registered the filter or the wrapper isn’t present, they safely return an empty string. The encoding falls back to UTF-8 when the request or response doesn’t declare a charset:

private String getStringValueFromBuffer(byte[] buffer, String encoding) {
    if (buffer.length > 0) {
        try {
            return new String(buffer, encoding != null ? encoding : StandardCharsets.UTF_8.name());
        } catch (UnsupportedEncodingException ex) {
            return "[unknown-encoding]";
      }
    }
    return "";
}

We use this in the request extraction:

private String extractRequestBody(HttpServletRequest request) {
    if (request instanceof ContentCachingRequestWrapper wrapper) {
        byte[] buf = wrapper.getContentAsByteArray();
        return getStringValueFromBuffer(buf, request.getCharacterEncoding());
    }
    return "";
}

And we use it in the response extraction as well:

private String extractResponseBody(HttpServletResponse response) {
    if (response instanceof ContentCachingResponseWrapper wrapper) {
        byte[] buf = wrapper.getContentAsByteArray();
        return getStringValueFromBuffer(buf, response.getCharacterEncoding());
    }
    return "";
}

Notably, ContentCachingRequestWrapper doesn’t eagerly copy the request body. It only fills its cache when something actually reads the input stream, which happens when Spring’s HttpMessageConverter deserializes the @RequestBody.

4.2. Registering the HandlerInterceptor

Now the final thing left to do is to register this handler for all the incoming API calls:

@Configuration
public class WebConfig implements WebMvcConfigurer {
    private final HttpLoggingInterceptor loggingInterceptor;
    public WebConfig(HttpLoggingInterceptor loggingInterceptor) {
        this.loggingInterceptor = loggingInterceptor;
    }
    @Override
    public void addInterceptors(InterceptorRegistry registry) {
        registry.addInterceptor(loggingInterceptor).addPathPatterns("/api/**");
    }
}

This configuration keeps logging separate from controller code and guarantees that all requests under /api/** pass through our HttpLoggingInterceptor.

4.3. Testing the Implementation

To verify our setup, we can write an integration test using MockMvc together with Spring Boot’s OutputCaptureExtension, which lets us assert against the log output produced during the test:

@SpringBootTest
@AutoConfigureMockMvc
@ExtendWith(OutputCaptureExtension.class)
class BookControllerLoggingUnitTest {
    @Autowired
    private MockMvc mockMvc;
    @Test
    void whenCreateBook_thenRequestAndResponseAreLogged(CapturedOutput output) throws Exception {
        String requestBody = """
          { "title": "Spring in Action", "author": "Craig Walls" }
        """;
        mockMvc.perform(post("/api/books")
          .contentType(MediaType.APPLICATION_JSON)
          .content(requestBody))
          .andExpect(status().isCreated());
        assertThat(output).contains("Incoming POST /api/books");
        assertThat(output).contains("HTTP POST /api/books status=201");
        assertThat(output).contains("\"title\":\"Spring in Action\"");
        assertThat(output).contains("\"author\":\"Craig Walls\"");
    }
}

5. Conclusion

In this article, we’ve seen how to log HTTP request and response bodies in a Spring MVC application without breaking @RequestBody deserialization.

We started by reviewing the request flow through the execution chain and the one-shot nature of HTTP body streams.

We then used ContentCachingRequestWrapper and ContentCachingResponseWrapper within a OncePerRequestFilter to buffer the request and response bodies, and finally read those buffers from a HandlerInterceptor in afterCompletion(), once the message converters had run and the response was complete.

As always, the source code for this article is available over on GitHub.

       

1. Overview

Tool-call reasoning improves the observability of AI agents by capturing the rationale behind LLM tool selection rather than simply recording which tool the model invokes.

Insight into agentic tool selection decisions helps developers debug agentic workflows, validate decision quality, and identify opportunities for prompt or tool design improvements. This transparency becomes increasingly important as agents grow more autonomous and interact with larger sets of tools.

In this tutorial, we’ll look at how the Embabel Agentic AI framework can be used to obtain this level of observability in our Java applications.

2. Tool Call Reasoning Using Spring AI

Before diving into how to use Embabel, let’s first consider how the problem is solved in the Spring AI Tool Argument Augmenter.

The Tool Argument Augmenter dynamically augments tool schemas with additional reasoning fields, such as innerThought or confidence. The result is improved observability into agent behavior without changing existing tool implementations.

In Spring AI, tools are defined using a JSON Schema that specifies the expected arguments. The LLM evaluates these schemas and decides which tool to invoke and which arguments to pass. Spring AI’s augmentation layer exploits this mechanism by extending the schema’s properties with additional properties visible to the model (i.e., schema augmentation).

An essential implementation feature is that the real tool definition, applied at the time of tool invocation, remains unchanged. The augmentation layer intercepts the tool-call payload, extracts the reasoning fields, removes them, and invokes the actual tool with only the expected arguments. The tool itself remains unaware of the augmentation.

The user receives tool-call reasoning details via a separate DTO (with properties innerThought and confidence).

2.1. Spring AI Solution Design Analysis

The design is strong in several areas.

First, it provides structured and machine-readable observability. Because reasoning fields are part of the schema, they are easier to evaluate, store, or feed into analytics pipelines.

Second, it remains largely non-invasive. Existing tools, including MCP tools and local @Tool methods, continue to work without modification.

Third, the solution leverages existing LLM capabilities instead of inventing proprietary reasoning APIs. Any model that supports structured tool calling can, in principle, support this approach.

However, the design also introduces trade-offs; a key limitation is that the exposed schema is no longer the actual contract. The LLM-facing schema contains fields that the underlying tool never actually accepts. Architecturally, this means that an augmented tool schema is not the same as the runtime tool method contract.

The approach introduces additional complexity through:

• Augmented DTOs
• Schema merging
• Augmented fields extraction
• Tool-wrapping infrastructure

This increases both conceptual overhead and coupling with Spring AI’s internal JSON Schema tool model.

3. Tool Call Reasoning Using a System Prompt

A simpler alternative is to define a standardized system-level protocol via prompting. When invoking a tool, the prompt should request the LLM to:

• Explain briefly why the tool is needed
• State what information is expected
• Provide a confidence level

This shifts reasoning capture from a schema-level contract to a prompt-level protocol—from structural enforcement to semantic guidance. In practice, schema augmentation enforces a hard contract (“fields must exist”), while prompting establishes a soft convention (“behave this way”).

Both approaches rely on model-generated explanations and therefore share the same variability in content and phrasing. The practical difference lies in output structure: schema augmentation enforces the presence of explicit fields, making explanations easier to capture and parse, whereas a prompt-based approach leaves them in semi-structured format.

The main advantage of the prompt-based approach is portability. Schema augmentation depends on framework-specific mechanisms (e.g., tool schema generation, callback providers, argument interception), whereas a system prompt relies only on a universal capability: sending instructions to the model. This makes it portable across other agentic frameworks such as Spring AI, LangChain4j, Semantic Kernel, OpenAI, Anthropic, and Google DeepMind.

Compared to the Spring AI augmentation approach, the prompt-based solution keeps the implementation simpler by avoiding synthetic schemas, augmentation DTOs, schema transformation, and custom tool-wrapping infrastructure.

Neither approach is universally superior—they embody different engineering trade-offs. In practice, a hybrid model often works best: use prompt-level protocols for general guidance and apply schema-level augmentation selectively where stronger guarantees are needed.

4. Tool Call Reasoning with Embabel

Embabel is an Agentic AI framework founded by Rod Johnson, creator of the Spring Framework. For introductory information about Embabel, please refer to the Baeldung article “Using the Embabel Agent Framework”. You can find the official Embabel documentation here, and more resources on Medium.

4.1. Quick Introduction to Embabel Modes

Embabel is a comprehensive framework for developing Agentic AI applications on the JVM. A key feature of Embabel is the employment of a fluent API with structured outputs in all modes – an important feature in the Agentic AI landscape for the JVM:

• Basic mode (without “thinking” or streaming)
• Thinking mode (details to follow)
• Streaming mode

We’ll explore the “thinking” mode, which serves as the foundation for the Tool Call Reasoning solution.

4.2. Embabel Agentic AI Dependencies

First, let’s define the major Embabel dependencies required for testing:

<!-- Embabel Agent Starter -->
<dependency>
    <groupId>com.embabel.agent</groupId>
    <artifactId>embabel-agent-starter-openai</artifactId>
    <version>${embabel-agent.version}</version>
</dependency>
<!-- Embabel Agent Test Support -->
<dependency>
    <groupId>com.embabel.agent</groupId>
    <artifactId>embabel-agent-test-internal</artifactId>
    <version>${embabel-agent.version}</version>
    <scope>test</scope>
</dependency>

The embabel-agent-starter-openai dependency provides us with the core classes required to use OpenAI models with Embabel.

We also include embabel-agent-test-internal to allow us to use the AgentTestApplication test support class with our @SpringBootTest.

4.3. Thinking Mode Fluent API Concepts

Let’s introduce key concepts of the Embabel “thinking” mode fluent API:

ThinkingResponse<ParkingRecommendation> result = ai.withDefaultLlm()
  .withToolObject(tools)
  .withToolLoopInspectors(callbackTracker, loggingInspector)
  .withToolLoopTransformers(systemMessageTransformer)
  .thinking()
  .createObject(prompt, ParkingRecommendation.class);

Ai is a gateway to LLM connectivity; we can define the default Ai LLM in the configuration file or supply it explicitly.

ThinkingResponse is a wrapper object that includes a reference to the underlying business object, ParkingRecommendation, and so-called ThinkingBlocks with LLM reasoning.

withToolObject() is a reference to the ParkingTooling tool object (with details later in the article).

withToolInspectors() refers to optional callbacks, helpful for additional logging, metrics collection, etc. CallbackTracker collects statistics (counter of LLM calls, counter of tool calls, list of invoked tools), while ToolLoopLoggingInspector logs LLM and tool invocation details (for example: ToolLoopLoggingInspector – afterLlmCall: iteration=1, toolCalls=2, contentLength=1063, usage=prompt=834, completion=260)

withToolLoopTransformers()  demonstrates user-controlled conversation history management via ToolLoopTransformer. In this example, the transformer is configured with two system messages and always positions them at the top of the conversation history before LLM calls.

thinking() indicates a fluent API subtype as “thinking mode” and outputs ThinkingResponse, which wraps the business object, constructed by the API createObject().

Finally, the prompt variable contains our user message.

Thinking mode includes a few more APIs, including a very useful one, createObjectIfPossible(). In the scenario, if the LLM is not capable of creating the object, ThinkingResponse will still carry reasoning behind this fact.

4.4. Use Case

Our use case is an optimal parking solution finder subject to time and cost constraints. There would be three tools at our disposal:

• Free street parking locator
• Metered parking locator
• Garage reservation

The final thinking block explains the reasoning behind the recommendation, based on information gathered through probing multiple tools.

We will split the prompt into a generic, domain-neutral system message that can be used across a variety of business scenarios and a domain-specific user message.

Let’s specify the system message:

"""
CRITICAL WORKFLOW - Two-phase decision process:
=== PHASE 1: Tool Selection (First Response) ===
1. For EACH tool you plan to call, emit a SEPARATE <tool_use_reasoning> block:
    <tool_use_reasoning>
    Tool: [TOOL_NAME]
    Why THIS tool: [explain why this specific tool is needed]
    Information expected: [what this tool will reveal]
    Advantage over alternatives: [why this tool vs others]
    Confidence in this tool selection [confidence=0.XX]
    </tool_use_reasoning>
    Since you must call at least 2 tools, you must emit at least 2 separate blocks.
2. Call AT LEAST TWO tools to gather comprehensive information
    - You MUST call at least 2 tools to probe different aspects.
    - Tools are PROBES for information gathering, not final decisions.
    - Multiple probes provide better decision quality.
=== PHASE 2: Final Decision (After receiving tool results) ===
1. Emit final decision reasoning:
    <final_decision_reasoning>
    - What each tool probe revealed
    - How the probe results informed your analysis
    - Why you chose this option based on probe data and constraints
    Confidence: [confidence=0.XX]
    </final_decision_reasoning>
2. Then provide the final structured output
    - Your final recommendation should synthesize insights from multiple probes.
    - Never copy reasoning blocks into the final structured object.
REMINDER: One <tool_use_reasoning> block per tool call. At least 2 tools = at least 2 blocks.
Emit reasoning in BOTH phases.
"""

And let’s specify a user message:

"""
Scenario:
An advisor is driving to a client meeting in Midtown Manhattan.
Constraints:
- 30 minutes remain before the meeting starts
- arriving late is not acceptable
- the meeting is expected to last about 3 hours
Parking options:
- Street parking: free, but uncertain
- Metered parking: $5 per hour, typically limited to 2 hours
- Garage parking: $30 per hour, guaranteed availability
Important decision factors:
- available time before the meeting
- risk of arriving late
- trade-offs between street, metered, and garage parking
Recommend the best parking option.
Available tools: %s
"""

Next, we’ll supply the available tools.

4.5. Tool Definition

Let’s define the tool object:

static class ParkingTooling {
    @LlmTool(description = "Find free street parking. Uncertain and may take time.")
    public String findStreetParking(String location, int maxMinutes) {
        boolean found = ThreadLocalRandom.current().nextDouble() < 0.3; // low probability
        if (found) {
            return "Street parking found near " + location + " (free)";
        }
        return "No street parking found within " + maxMinutes + " minutes";
    }
    @LlmTool(description = "Find metered parking. Moderate cost and moderate availability. " +
        "May have time limits.")
    public String findMeterParking(String location, int maxMinutes) {
        boolean found = ThreadLocalRandom.current().nextDouble() < 0.6; // medium probability
        if (found) {
            return "Metered parking found near " + location + " ($5/hour, 2-hour limit)";
        }
        return "No metered parking found within " + maxMinutes + " minutes";
    }
    @LlmTool(description = "Reserve guaranteed garage parking near destination.")
    public String reserveGarage(String location) {
        return "Garage reserved near " + location + " ($30/hour, guaranteed)";
    }
}

Here, LlmTool is an Embabel tool annotation, which includes a tool description. Embabel allows us to expose tools to the LLM by implementing the Embabel Tool interface, or using annotated Java methods. Here, we see the latter approach.

4.6. Test Results Discussion

Our test ToolCallReasoningIntegrationTest invokes the LLM (which in turn invokes tools and summarizes tool selection recommendations) and logs results using custom logging functions.

We have a reportToolCallPattern() method that takes CallbackTracker as a parameter and logs tool pattern and statistics:

17:26:33.876 [main] INFO  ToolCallReasoningIntegrationTest - === TOOL CALL PATTERN ANALYSIS ===
Total LLM calls: 2
Unique tools: [findStreetParking, findMeterParking]
Total invocations: 2
Tool result callbacks: 2
Iterations with tool calls: 1
17:26:33.876 [main] INFO  ToolCallReasoningIntegrationTest - PATTERN: All 2 tools called in SAME iteration (parallel)
17:26:33.876 [main] INFO  ToolCallReasoningIntegrationTest - Thinking blocks captured: 3
17:26:33.876 [main] INFO  ToolCallReasoningIntegrationTest -   Block 1: tagType=TAG, tagValue=tool_use_reasoning, contentLength=481
17:26:33.877 [main] INFO  ToolCallReasoningIntegrationTest -     Tool: functions.findStreetParking
17:26:33.877 [main] INFO  ToolCallReasoningIntegrationTest -     Attributes: {confidence=0.85}
17:26:33.877 [main] INFO  ToolCallReasoningIntegrationTest -   Block 2: tagType=TAG, tagValue=tool_use_reasoning, contentLength=494
17:26:33.877 [main] INFO  ToolCallReasoningIntegrationTest -     Tool: functions.findMeterParking
17:26:33.877 [main] INFO  ToolCallReasoningIntegrationTest -     Attributes: {confidence=0.90}
17:26:33.877 [main] INFO  ToolCallReasoningIntegrationTest -   Block 3: tagType=TAG, tagValue=final_decision_reasoning, contentLength=755
17:26:33.877 [main] INFO  ToolCallReasoningIntegrationTest -     Attributes: {confidence=0.95}

Recommended: ParkingRecommendation[chosenOption=GARAGE, location=Midtown Manhattan, estimatedTotalCost=90, summary=Street parking is unavailable within 30 minutes and metered parking is limited to 2 hours, insufficient for a 3-hour meeting. Garage parking offers guaranteed availability and ensures timely arrival, making it the best option despite higher cost.]
Reasoning:
  ThinkingBlock(content=Tool: functions.findStreetParking
Why THIS tool: To check the availability and likelihood of finding free street parking near Midtown Manhattan within the 30-minute time constraint.
Information expected: Whether street parking is realistically available and how long it might take to find a spot.
Advantage over alternatives: Street parking is free and the cheapest option, but highly uncertain. Testing its feasibility is key before considering costlier options.
Confidence in this tool selection [confidence=0.9], tagType=TAG, tagValue=tool_use_reasoning)
  ThinkingBlock(content=Tool: functions.findMeterParking
Why THIS tool: To evaluate availability and feasibility of metered parking given the 2-hour limit and $5/hour cost within the 30-minute timeframe.
Information expected: Confirm if metered parking spots are available nearby and potential cost and time constraints.
Advantage over alternatives: Metered parking balances moderate cost and availability but with time limits, important to check if it fits the meeting duration.
Confidence in this tool selection [confidence=0.9], tagType=TAG, tagValue=tool_use_reasoning)
  ThinkingBlock(content=- The street parking probe revealed no available free street parking within the critical 30-minute arrival window, making this option too risky given the zero tolerance for lateness.
- Metered parking is available near the destination, but limited to 2 hours and costs $5/hour. This time limit is insufficient for the 3-hour meeting, so this option creates the risk of parking violation or disruption.
- Although not probed yet, garage parking guarantees availability and can cover the full 3-hour meeting, eliminating risk of lateness or parking issues at a higher cost ($30/hour).
- Given the importance of timely arrival and the meeting length, garage parking is the most reliable and suitable choice despite the cost premium.
Confidence: [confidence=0.95], tagType=TAG, tagValue=final_decision_reasoning)

Embabel returned a structured object of type ParkingRecommendation with all expected fields, including summary. 

Each ThinkingBlock includes an explanation of the tool selection, a tagType specifies the LLM reasoning format – in our case, this is XML TAG (could be PREFIX, or no tag), a tagValue=tool_use_reasoning, and a tagValue=final_decision_reasoning, which are the dynamic user-defined XML tags we specified in the system prompt. Please note two ThinkingBlocks, one for each tool call.

Finally, the ThinkingBlock with the recommendation was emitted, with placeholder confidence substituted, as expected.

The use of placeholders permits further automated processing for tool selection reasoning. The method extractAttributes() (please refer to the code linked to the article) scans a ThinkingBlock for attributes in [key=value] format and returns them as a Map of key-value pairs. For example, it converts text like [confidence=0.9] into an entry such as “confidence” -> 0.9.

Thus, Embabel’s thinking mode output is semi-structured.

5. Conclusion

In this article, we saw how Embabel provides a solution for tool selection reasoning by employing its “thinking” APIs.

While Spring AI provides out-of-the-box support for Tool Selection Reasoning, the solution has noticeable trade-offs. An alternative solution using Embabel is very simple and, unlike Spring AI, does not require hidden schema manipulations or the introduction of a custom DTO, and is fully built on the existing Embabel foundation APIs.

With Embabel, yet another solution using LLM Tool Loop callbacks is available, but it is outside the scope of this article.

The article code is available over on GitHub.

The author would like to thank Rod Johnson and Ajen Poutsma for their reviews and suggestions.

       

1. Overview

2. The HandlerInterceptor Interface

public interface FeatureFlagService {
    int rolloutPercentage();
}

public class DeliveryChargeInterceptor implements HandlerInterceptor {
    static final String USE_V2_ATTRIBUTE = "useV2";
    private final FeatureFlagService featureFlagService;
    private final SecureRandom random = new SecureRandom();
    private final Logger logger = LoggerFactory.getLogger(
      DeliveryChargeInterceptor.class
    );
    public DeliveryChargeInterceptor(FeatureFlagService featureFlagService) {
        this.featureFlagService = featureFlagService;
    }
    @Override
    public boolean preHandle(
      HttpServletRequest request,
      HttpServletResponse response,
      Object handler) throws Exception {
        if (handler instanceof HandlerMethod) {
            int rollout = featureFlagService.rolloutPercentage();
            boolean useV2 = rollout > 0 && random.nextInt(100) < rollout;
            request.setAttribute(USE_V2_ATTRIBUTE, useV2);
            logger.info(
              "Delivery charge feature: rollout={}%, useV2={}",
              rollout,
              useV2
            );
        }
        return true;
    }
}

@RestController
public class DeliveryChargesController {
    private final DeliveryChargeService deliveryChargeService;
    public DeliveryChargesController(DeliveryChargeService deliveryChargeService) {
        this.deliveryChargeService = deliveryChargeService;
    }
    @PostMapping("/delivery-charges/calculate")
    public double calculate(
      @RequestParam String postcode,
      HttpServletRequest request) {
        Boolean useV2 = (Boolean) request.getAttribute(
          DeliveryChargeInterceptor.USE_V2_ATTRIBUTE
        );
        if (Boolean.TRUE.equals(useV2)) {
            return deliveryChargeService.calculateV2(postcode);
        }
        return deliveryChargeService.calculateV1(postcode);
    }
}

@Configuration
public class WebMvcConfig implements WebMvcConfigurer {
    private final FeatureFlagService featureFlagService;
    public WebMvcConfig(FeatureFlagService featureFlagService) {
        this.featureFlagService = featureFlagService;
    }
    @Override
    public void addInterceptors(InterceptorRegistry registry) {
        registry.addInterceptor(new DeliveryChargeInterceptor(featureFlagService))
          .addPathPatterns("/delivery-charges/**");
    }
}

@WebMvcTest(controllers = DeliveryChargesController.class)
class DeliveryChargeInterceptorIntegrationTest {
    @Autowired
    private MockMvc mockMvc;
    @MockBean
    private DeliveryChargeService deliveryChargeService;
    @MockBean
    private FeatureFlagService featureFlagService;
}

@Test
void givenZeroRollout_whenCalculateDeliveryCharge_thenV1IsUsed() throws Exception {
    when(featureFlagService.rolloutPercentage()).thenReturn(0);
    when(deliveryChargeService.calculateV1("SW1A")).thenReturn(5.0);
    mockMvc.perform(post("/delivery-charges/calculate").param("postcode", "SW1A"))
      .andExpect(status().isOk())
      .andExpect(content().string("5.0"));
}

@Test
void givenFullRollout_whenCalculateDeliveryCharge_thenV2IsUsed() throws Exception {
    when(featureFlagService.rolloutPercentage()).thenReturn(100);
    when(deliveryChargeService.calculateV2("SW1A")).thenReturn(3.5);
    mockMvc.perform(post("/delivery-charges/calculate").param("postcode", "SW1A"))
      .andExpect(status().isOk())
      .andExpect(content().string("3.5"));
}

@Test
void givenPartialRollout_whenCalculateDeliveryCharge_thenBothVersionsAreUsed() throws Exception {
    when(featureFlagService.rolloutPercentage()).thenReturn(50);
    when(deliveryChargeService.calculateV1("SW1A")).thenReturn(5.0);
    when(deliveryChargeService.calculateV2("SW1A")).thenReturn(3.5);
    int v1Count = 0;
    int v2Count = 0;
    for (int i = 0; i < 20; i++) {
        String response = mockMvc.perform(
            post("/delivery-charges/calculate")
              .param("postcode", "SW1A")
          )
          .andExpect(status().isOk())
          .andReturn()
          .getResponse()
          .getContentAsString();
        if ("5.0".equals(response)) {
            v1Count++;
        } else if ("3.5".equals(response)) {
            v2Count++;
        }
    }
    assertThat(v1Count).isGreaterThan(0);
    assertThat(v2Count).isGreaterThan(0);
}

       

1. Spring and Java

Also worth reading:

• >> Nulling Out References Won’t Help Your Garbage Collector [foojay.io]
• >> Hardwood Promises High-Speed JVM Apache Parquet Processing with Zero Mandatory Dependencies [infoq.com]
• >> What’s New in actions/setup-java 5.4 and 5.5: Signature Verification, Kona JDK, and a Better Maven Experience [foojay.io]
• >> Demystifying Exposed: The Intelligent SQL Library for Kotlin [foojay.io]
• >> The Java Story: A Film About All of Us [foojay.io]
• >> Introducing the Kotlin Benchmark for AI Coding Agents [jetbrains.com]
• >> A New Home for Spring Cloud Contract: Transitioning to Stubborn.sh [spring.io]

Webinars and presentations:

• >> Episode 61 “Scripting JS and Python with Project Detroit” [AtA] [inside.java]
• >> A Bootiful Podcast: Spring Boot legend Moritz Halbritter on the latest and greatest in Spring Boot 4 and 4.1 [spring.io]
• >> Spring Office Hours Podcast: S5E17 – Spring Boot 4.1 with Phil Webb [spring.io]

Time to upgrade:

• >> Quarkus 3.37.2 – Maintenance release [quarkus.io]
• >> Vert.x 4.5.29 and 5.1.4 [github.com/eclipse-vertx]
• >> Zuul v4.0.0, v3.6.21, v3.6.20, and v3.6.19 [github.com/Netflix]
• >> Micronaut Core 5.1.5, 5.1.4, 5.0.5, and 4.10.26 [github.com/micronaut-projects]

2. Technical & Musings

Also worth reading:

• >> Two nasty surprises in Home Assistant’s config [frankel.ch]
• >> Better Models: Worse Tools [lucumr.pocoo.org]
• >> Airbnb Shares Architecture behind Sitar-Agent Dynamic Configuration Sidecar for Kubernetes Services [infoq.com]
• >> Experiences with local models for coding [martinfowler.com]
• >> Viability of local models for coding [martinfowler.com]
• >> GLM-5.2: Considerations for enterprise teams starting out with open-weight models [scottlogic.com]
• >> Auditable Agentic Orchestration: From Autonomous Systems to Governed Execution [scottlogic.com]

3. Pick of the Week

One more week:

>> The Summer Sale is Live

       

1. Overview

When writing SQL queries, we often need to build the query dynamically. For instance, dynamic string building is usually required when writing logic for filters, runtime-specified sorts, and optional join operations.

In this tutorial, we’ll see how Java creates SQL strings. Notably, we’ll use the Baeldung Simple University Schema.

2. The Challenge of Dynamic Queries

Let’s suppose we’re building a student search feature. Consequently, a user can search for students using one or more optional filters:

• name
• student ID
• enrollment date
• others

Since filters may vary depending on search criteria, we need different or dynamic SQL queries.

If no filters are selected, we simply retrieve all students:

SELECT * FROM Student;

However, if we search only by name, the query becomes:

SELECT * FROM Student WHERE name LIKE ?;

If we search by both name and student ID, we need an additional condition:

SELECT * FROM Student WHERE name LIKE ? AND id = ?;

Finally, if all filters are provided, the query becomes:

SELECT * FROM Student WHERE name LIKE ? AND id = ?
AND enrollment_date = ?;

As the query grows, it becomes hard to maintain:

String sql = "SELECT s.id, s.name, s.national_id, s.birth_date, " 
  + "s.enrollment_date, s.graduation_date, s.gpa " 
  + "FROM students s " 
  + "WHERE s.gpa >= " + minGpa + " " 
  + "AND s.enrollment_date >= '" + enrollmentDate + "' " 
  + (includeGraduated ? "" : "AND s.graduation_date IS NULL ") 
  + "ORDER BY s.gpa DESC, s.name";

As a result, readability becomes a key issue.

3. Dynamic SQL Generation

When querying a database, users often sort the results with different filters. For example, they may choose to sort by name or date. Thus, we should be able to create an SQL query with dynamic parameters.

3.1. Using the StringBuilder Class

The StringBuilder class in Java enables changes to string objects that are normally immutable in the String class.

Thus, we can assemble SQL queries in steps:

StringBuilder sb = new StringBuilder("SELECT id, name FROM Student");
if (enrollmentDate != null) {
    sb.append(" WHERE enrollment_date = ?");
}
if (birthDate != null) {
    sb.append(" AND birth_date = ?");
}

Still, the above solution isn’t complete. For example, if the user provides a null value to enrollment_date, the WHERE clause doesn’t complete:

SELECT id, name FROM Student AND birth_date = ?

Thus, the resulting SQL becomes invalid.

3.2. Building Conditions Separately

Let’s consider another variant of the above code:

StringBuilder sb = new StringBuilder("SELECT id, name " + "FROM Student WHERE 1=1");
...

In this case, we start with a new clause WHERE 1=1, which is always true. So, we can now start every conditional statement with AND:

if (enrollmentDate != null) {
    sb.append(" AND enrollment_date = ?");
}
if (birthDate != null) {
    sb.append(" AND birth_date = ?");
}

The parameters are now dynamically added to the SQL query. Lastly, we can execute this query using PreparedStatement.

3.3. Execution Using PreparedStatement

Since we’ve used parameterized queries in the above code, we can use PreparedStatement to execute the query:

ps.setString(parameterIndex++, enrollmentDate);
ps.setString(parameterIndex++, birthDate);

In the above code, ps is the PreparedStatement object.

4. Using StringJoiner (Java 8)

The StringJoiner class in Java can join multiple strings. Thus, we can use it to connect multiple SQL strings:

String baseQuery = "SELECT * FROM Student";
StringJoiner whereClause = new StringJoiner(" AND ");
whereClause.add("name LIKE ?");
whereClause.add("id = ?");
whereClause.add("enrollment_date = ?");

In the above example, the search criteria (name, id, and enrollment_date) are dynamic and optional. As a result, we can dynamically build a SQL WHERE clause.

5. Conclusion

In this article, we went through building a SQL query string in Java. First, we saw how long queries become unmanageable. Then, we looked at how the StringBuilder and StringJoiner classes help build dynamic SQL queries.

The code backing this article is available over on GitHub.

       

1. Overview

With Spring AI, we can integrate LLMs into our Spring applications. This is helpful, for example, for processing unstructured data like wiki pages, emails, or chat messages by extracting structured information to store it in a database. However, LLM responses are not deterministic and so neither testable nor handable by our application code. The best way to implement a kind of quality gate for LLM responses is to use an LLM again for evaluation. This pattern is called LLM-as-a-judge.

In this tutorial, we’ll learn how to implement the LLM-as-a-Judge pattern in Spring AI.

2. Maven Dependencies

We add the Spring AI OpenAI starter to our pom.xml:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-model-openai</artifactId>
    <version>${spring-ai.version}</version>
</dependency>

The latest version of spring-ai-starter-model-openai is available on Maven Central.

3. The LLM-as-a-Judge Pattern

The LLM-as-a-Judge pattern is like a code review. A developer (the generator) writes code, and a senior engineer (the judge) reviews it and gives structured feedback. If the judge decides that the code doesn’t meet the requirements, the developer needs to improve it, and the judge then needs to review it again.

Applied to LLMs, the process is:

• The generator produces an initial answer to the user’s question
• The judge evaluates that answer against a rubric and returns a numeric score and written feedback
• If the score is too low, the generator receives the original question plus the judge’s critique and produces a refined answer.
• This will repeat until the answer meets the requirements. To avoid endless repetition in case of never reaching the needed quality, it’s a best practice to limit the number of loops.

This pattern improves output quality without model fine-tuning, without a human in the loop, and without changing a single line of calling code.

It’s most valuable wherever a weak answer has real consequences, e.g., customer support bots, where a vague answer to a billing question damages trust directly. In such cases, the pattern acts as an invisible quality gate between the model and the user.

3.1. Why Recursive Advisors Are the Right Fit

Advisors in Spring AI are interceptors that wrap the request/response cycle of a ChatClient. A CallAroundAdvisor can inspect a request before it reaches the model and modify the response before it reaches the caller, but it only ever makes one model call per invocation.

Recursive advisors go further: they hold an internal ChatClient reference and can make additional model calls from within the advisor itself.

We can apply that capability to a specific problem: using the second model call as a judge, not just an extension. The generate-judge-refine loop becomes a single, self-contained component that’s completely transparent to the caller.

The caller just invokes chatClient.prompt().user(“…”).call().content() as usual. The judge’s logic is invisible.

4. Implementing the LLM-as-a-Judge Advisor

Let’s build the feature in three parts: a value object for the verdict, a prompt template for the judge, and the advisor itself.

4.1. The Verdict Record

The judge returns a structured verdict. We model it as a simple Java record:

public record Verdict(double score, String feedback) {}

In our sample, the score is a value between 0.0 (poor) and 1.0 (excellent). The feedback explains what’s missing or weak, and it’s what we’ll feed back to the generator during refinement.

4.2. The Judge Prompt Template

The judge needs a clear, structured system prompt. For this, let’s define it as a constant inside the advisor:

private static final String JUDGE_SYSTEM_PROMPT = """
    You are a strict quality evaluator for AI-generated answers.
    
    Given a user question and an AI-generated answer, rate the answer quality.
    
    Use this rubric:
    - 1.0: Complete, accurate, and clearly explained
    - 0.7: Mostly correct but missing details or clarity
    - 0.4: Partially correct or overly vague
    - 0.0: Incorrect, irrelevant, or harmful
    
    Respond ONLY with a valid JSON object. Do not add any explanation outside the JSON.
    Format: {"score": <0.0 to 1.0>, "feedback": "<one concise sentence>"}
    """;

It’s important to declare an explicit rubric and a strict JSON output to get a parseable and consistent answer from the judge model.

4.3. The Advisor Class and Its Dependencies

The key design decision of a recursive advisor is that it holds its own ChatClient instance to make additional model calls independently of the advisor chain. We also inject the configurable quality thresholds:

public class LlmJudgeAdvisor implements CallAdvisor {
    private final ChatClient judgeClient;
    private final double scoreThreshold;
    private final int maxRefinements;
    public LlmJudgeAdvisor(
      ChatClient judgeClient,
      double scoreThreshold,
      int maxRefinements
    ) {
        this.judgeClient = judgeClient;
        this.scoreThreshold = scoreThreshold;
        this.maxRefinements = maxRefinements;
    }
    @Override
    public int getOrder() {
        return 0;
    }
    @Override
    public String getName() {
        return "LlmJudgeAdvisor";
    }
    // ...
}

The ChatClient.Builder receives the same auto-configured model settings as the rest of the application. We could wire a different, specialized judge model here. The official Spring AI docs recommend this specifically to avoid the model judging its own output too leniently.

4.4. Evaluating and Refining the Response

The adviseCall() method is the only entry point that the advisor chain calls. Rather than forwarding the request once, we call chain.copy(this).nextCall(request), which creates a sub-chain that includes our advisor again. That’s what enables the loop to re-evaluate and re-generate across multiple attempts:

@Override
public ChatClientResponse adviseCall(ChatClientRequest request, CallAdvisorChain chain) {
    for (int attempt = 1; attempt <= maxRefinements + 1; attempt++) { 
        ChatClientResponse response = chain.copy(this).nextCall(request);
        if (attempt > maxRefinements) {
          return response;
        }
        Verdict verdict = evaluate(request, response); 
        if (verdict.score() >= scoreThreshold) {
            return response;
        }
        request = addFeedback(request, verdict.feedback());
    }
    return chain.copy(this).nextCall(request);
}

The loop needs an explicit upper bound to avoid unbounded recursion. On the last allowed attempt, the method returns the response immediately, skipping the evaluation. There’s no point scoring an answer we can’t act on. Otherwise, if the score meets scoreThreshold, it returns early. If not, it augments the request with the judge’s feedback and continues to the next iteration.

4.5. Evaluating the Answer

The evaluate() method sends the original question and the generated answer to the judge model. We can use  .entity(…) to render the response JSON directly into our record, without any manual JSON parsing:

private Verdict evaluate(ChatClientRequest request, ChatClientResponse response) {
    String question = request.prompt().getUserMessage().getText();
    String answer = response.chatResponse().getResult().getOutput().getText();
    return judgeClient.prompt()
      .system(JUDGE_SYSTEM_PROMPT)
      .user("Question: " + question + "\n\nAnswer: " + answer)
      .call()
      .entity(Verdict.class);
}

Spring AI injects a JSON schema derived from the Verdict record into the model call and handles deserialization. This removes the need for any try-catch around JSON parsing and keeps the evaluation code focused on the prompt, not on format handling.

4.6. Augmenting the Request With Feedback

When the rating falls short, we don’t start over with the original prompt. Instead, let’s augment it, i.e., we append the judge’s critique to the user message so the model has full context for its next attempt. The addFeedback() helper does this using ChatClientRequest.mutate():

private ChatClientRequest addFeedback(ChatClientRequest original, String feedback) {
    Prompt augmented = original.prompt()
      .augmentUserMessage(msg -> msg.mutate()
          .text(msg.getText()
              + "\n\nYour previous answer was insufficient. Feedback: " + feedback
              + "\nPlease provide an improved answer.")
          .build());
    return original.mutate().prompt(augmented).build();
}

augmentUserMessage() gives us a mutable copy of the user message without touching the rest of the request. System prompt, tool definitions, and conversation history all stay intact. The updated ChatClientRequest is passed into the next loop iteration.

5. Configuring the Application

Let’s declare both beans in a single @Configuration class. The LlmJudgeAdvisor bean receives its own ChatClient instance, built from the same auto-configured ChatClient.Builder, along with the quality thresholds from application.properties:

@Configuration
public class ChatConfig {
    @Bean
    public ChatClient chatClient(
      ChatClient.Builder builder, 
      LlmJudgeAdvisor judgeAdvisor
    ) {
        return builder
          .defaultAdvisors(judgeAdvisor)
          .build();
    }
    @Bean
    public LlmJudgeAdvisor llmJudgeAdvisor(
      ChatClient.Builder builder,
      @Value("${judge.score-threshold:0.7}") double scoreThreshold,
      @Value("${judge.max-refinements:2}") int maxRefinements
    ) {
        return new LlmJudgeAdvisor(
          builder.build(),
          scoreThreshold,
          maxRefinements
        );
    }
}

We can then set the thresholds in application.properties:

spring.ai.openai.api-key=${OPENAI_API_KEY}
spring.ai.openai.chat.options.model=gpt-4o-mini
judge.score-threshold=0.75
judge.max-refinements=2

A score threshold of 0.75 means the judge must rate the answer at least 75% quality before we accept it. Setting the number of maximum refinements to 2 caps the recursive loop at two improvement attempts, preventing runaway API usage.

6. Testing the Advisor

Let’s implement a @SpringBootTest with a single mocked ChatModel. Both the generator and the judge use the same underlying instance, so one mock covers the entire call sequence. No API key is needed.

The test stubs four sequential chatModel responses matching the expected flow:

@SpringBootTest
class LlmJudgeAdvisorTest {
    @MockitoBean
    ChatModel chatModel;
    @Autowired
    ChatClient chatClient;
    @Test
    void givenLowQualityAnswer_whenAdvisorRuns_thenAnswerIsRefined() {
        when(chatModel.call(any(Prompt.class)))
          .thenReturn(buildChatResponse("It runs Java."))
          .thenReturn(buildChatResponse("{\"score\": 0.3, \"feedback\": \"Too vague.\"}"))
          .thenReturn(buildChatResponse(
            "The JVM executes Java bytecode, manages memory, and enables platform independence."))
          .thenReturn(buildChatResponse("{\"score\": 0.9, \"feedback\": \"Complete and accurate.\"}"));
        String result = chatClient.prompt()
          .user("Explain what a JVM is.")
          .call()
          .content();
        assertThat(result).contains("bytecode");
    }
    private ChatResponse buildChatResponse(String content) {
        return new ChatResponse(List.of(new Generation(new AssistantMessage(content))));
    }
}

The four stubs map directly to the generate-evaluate-refine-evaluate cycle: call one is the weak generator response, call two is the judge’s low verdict, call three is the refined generator response, and call four is the judge’s passing verdict. The chatClient with the advisor is real. Only the model underneath is mocked. This means the full advisor logic runs as it would in production.

7. Conclusion

In this article, we implemented the LLM-as-a-Judge pattern in Spring AI using a recursive CallAdvisor. We saw how the generate-evaluate-refine loop maps naturally onto the advisor model: the first model call produces an answer, Spring AI’s structured output maps the judge’s response into a typed Verdict, and the loop augments the original request with feedback until the score is sufficient or the attempt limit is reached.

The result is a reusable component that improves output quality automatically and remains completely transparent to any caller using the ChatClient.

The code for this tutorial is available over on GitHub.

       

Comments

• In reply to fantaman. Hey, Fantaman. Thanks for the ... by Ulisses Lima
• Wouldn't it make sense to also include the LLM answer in the ... by fantaman

 

1. Spring and Java

>> A return to two-pizza culture [allthingsdistributed.com]

Revisiting Amazon’s two-pizza team idea as an operating model for fast, autonomous, accountable engineering. The practical point: team size, ownership, architecture, and feedback loops shape each other. A useful read.

Also worth reading:

• >> Security Baked Into the JVM: why fork Apache River and OpenJDK? [frankel.ch]
• >> This Dependency Update Looked Exactly Like an Account Takeover [foojay.io]
• >> 7 New Vulnerabilities in Jackson in One Day: This Is What AI-Assisted Security Research Looks Like [foojay.io]
• >> Endive 1.0 Is Here: Wasm on the JVM Ships Under the Bytecode Alliance [foojay.io]
• >> AI-Assisted Unused & Dead Code Removal [foojay.io]
• >> MongoDB-backed Spring Batch jobs and more in Spring Boot 4.1 [spring.io]
• >> CopyOnWriteArrayList.subList() ConcurrentModificationException [javaspecialists.eu]
• >> Scaling Java-Based Real-Time Systems: The Hidden Tradeoffs of Event-Driven Design [infoq.com]
• >> Eliya 25 Brings a JVM-Level Diagnostic Profile to OpenJDK 25 LTS [infoq.com]
• >> Open-Sourcing the LSP Client API in IntelliJ IDEA 2026.2 [jetbrains.com]
• >> Agentrification and the Agentrification Index [scottlogic.com]
• >> GenPage: Towards End-to-End Generative Homepage Construction at Netflix [netflixtechblog.com]

Webinars and presentations:

• >> SIMD Vectors in the HotSpot JVM – Auto Vectorization and the Vector API [inside.java]
• >> ZGC: A Decade of Redefining Java Performance [inside.java]
• >> A Bootiful Podcast: Sébastien Deleuze on the latest-and-greatest in Spring AI and Spring Framework [spring.io]

Time to upgrade:

• >> IntelliJ IDEA 2026.1.4 Is Out! [jetbrains.com]
• >> Quarkus 3.37.1 – Maintenance release [quarkus.io]
• >> GraalVM Community 25.1.3 [github.com/graalvm]
• >> Jetty 12.1.11 and 12.0.37 [github.com/jetty]
• >> Elasticsearch 8.19.18, 9.3.7, and 9.4.3 [github.com/elastic]
• >> Zuul v3.6.18 and v3.6.17 [github.com/Netflix]
• >> Grails 7.2.0, 7.1.3, and 7.0.13 [github.com/apache]
• >> Micronaut Core 5.1.3, 5.0.4, and 3.10.9 [github.com/micronaut-projects]
• >> Apache Camel 4.21.0, 4.18.3, and 4.14.8 [github.com/apache]

2. Pick of the Week

>> The Summer Sale is Live

       

1. Overview

When we build AI-integrated systems, we often provide our AI clients with a large number of tools. On every request, we send the definitions of all available tools to the LLM so it can decide which ones to use. As a result, we waste a significant number of tokens before the model even processes the user query.  In this article, we explore how we solve this issue using the Tool Search Tool. 

2. How the Tool Search Tool Works

Using the Tool Search Tool, we don’t send all the tool definitions with the context. We only expose tools when the model actually needs them. First, we index all registered tools at startup. We store them inside the ToolSearcher, but we do NOT send them to the LLM. Next, we send only the Tool Search Tool in the initial request. This keeps the prompt small and focused. When the model needs a capability, it calls the Tool Search Tool using a natural-language query.

We treat this as a discovery signal and trigger a search over the indexed tools using the configured strategy. Next, we return only the most relevant matches from the ToolSearcher and inject their definitions into the next LLM request, so the model sees a focused set of tools instead of the full registry.

Once the relevant tools are available, the model selects and calls the actual tool. We execute it and send the result back to the LLM, which then uses it to generate the final answer.

3. Building a Travel Assistant Example

Let’s build a travel assistant that helps users plan trips. We connect multiple tools such as flights, hotels, weather, and attractions. We use the Tool Search Tool approach to avoid sending all tools to the LLM upfront. Instead, we discover tools dynamically at runtime.

3.1. Dependencies

We start by adding tool search dependency support:

<dependency>
    <groupId>org.springaicommunity</groupId>
    <artifactId>tool-search-tool</artifactId>
    <version>${tool-search-tool.version}</version>
</dependency>

Also, let’s add the regex searcher dependency:

<dependency>
    <groupId>org.springaicommunity</groupId>
    <artifactId>tool-searcher-regex</artifactId>
    <version>${tool-search-tool.version}</version>
</dependency>

Using it, we’ll have a regex tool search strategy. The other available strategies can be found in the project repository.

3.2. Flight Tools

Let’s create a simple FlightTools. We’ll use this tool to retrieve available flight options. In addition, we’ll create a bunch of artificial tools to simulate context overloading:

public class FlightTools {
    @Tool(description = "Searches available flights between two cities")
    public List<FlightOption> searchFlights(String from, String to, String departureDate) {
        return List.of(
          new FlightOption(
            "Romania Airlines",
            from,
            to,
            departureDate,
            249.99
          )
        );
    }
}

Here we return a single flight option.

3.3. TokenCounterAdvisor

Now let’s create a simple TokenCounterAdvisor that counts the number of tokens used to produce the final result. We’ll use it to compare token usage between different setups, with and without tool search enabled:

public class TokenCounterAdvisor implements BaseAdvisor {
    private static final Logger log = LoggerFactory.getLogger(TokenCounterAdvisor.class);
    private final AtomicInteger totalTokenCounter = new AtomicInteger(0);
    @Override
    public String getName() {
        return "TokenCounterAdvisor";
    }
    @Override
    public int getOrder() {
        return Ordered.LOWEST_PRECEDENCE - 1;
    }
    @Override
    public ChatClientRequest before(ChatClientRequest chatClientRequest, AdvisorChain advisorChain) {
        return chatClientRequest;
    }
    @Override
    public ChatClientResponse after(ChatClientResponse chatClientResponse, AdvisorChain advisorChain) {
        var usage = chatClientResponse.chatResponse().getMetadata().getUsage();
        totalTokenCounter.addAndGet(usage.getTotalTokens());
        log.info("Total tokens spent: {}", totalTokenCounter.get());
        return chatClientResponse;
    }
}

Here we store the number of tokens in an AtomicInteger field and log this information during execution. We attach this advisor to the maximum order, so it runs at the end of the processing pipeline. As a result, it captures the total token usage after all other advisors complete.

3.4. Configuration

Next, we add the TravelAssistantConfig implementation:

@Configuration
public class TravelAssistantConfig {
    @Bean
    ToolSearcher toolSearcher() {
        return new RegexToolSearcher();
    }
    @Bean
    ToolSearchToolCallAdvisor toolSearchToolCallAdvisor(ToolSearcher toolSearcher) {
        return ToolSearchToolCallAdvisor.builder()
          .toolSearcher(toolSearcher)
          .maxResults(5)
          .build();
    }
    @Bean
    ChatClient chatClient(ToolSearchToolCallAdvisor toolSearchToolCallAdvisor, OpenAiChatModel model) {
        return ChatClient.builder(model)
          .defaultTools(
            new FlightTools(),
            new RandomTools()
          )
          .defaultAdvisors(toolSearchToolCallAdvisor, new TokenCounterAdvisor())
          .build();
    }
    @Bean
    ChatClient chatClientWithoutToolsSearch(OpenAiChatModel model) {
        return ChatClient.builder(model)
          .defaultTools(
            new FlightTools(),
            new RandomTools()
          )
          .defaultAdvisors(new TokenCounterAdvisor())
          .build();
    }
}

We configure a travel assistant that uses dynamic tool discovery instead of loading all tools into the LLM. Next, we set up a ToolSearcher with a RegexToolSearcher implementation. This allows us to match tools based on naming patterns and fast keyword-like queries. Then, we create a ToolSearchToolCallAdvisor and connect it to the searcher. After that, we build the ChatClient with the flight tools registered.

By design, we’ve added RandomTools, which includes many unrelated tool definitions.  However, we do not send these tool definitions to the LLM initially. Instead, we only index them in the system. Finally, we expose only the Tool Search Tool to the model at the start. The model then uses it to discover which tools it actually needs for a given request. Additionally, we’ve configured a separate ChatClient bean that doesn’t use the ToolSearchToolCallAdvisor.

3.5. Call the TravelAssistant

Finally, let’s create a ToolsSearchToolLiveTest with similar test cases for both clients:

@SpringBootTest
@ActiveProfiles("toolsearchtool")
class ToolsSearchToolLiveTest {
    @Autowired
    private ChatClient chatClient;
    @Autowired
    private ChatClient chatClientWithoutToolsSearch;
    @Test
    void shouldFindFlightsBetweenRomaniaAndCroatiaUsingToolsSearch() {
        String response = getClientResponseString(chatClient);
        assetClientResponse(response);
    }
    @Test
    void shouldFindFlightsBetweenRomaniaAndCroatiaWithoutToolsSearch() {
        String response = getClientResponseString(chatClientWithoutToolsSearch);
        assetClientResponse(response);
    }
    
    private static void assetClientResponse(String response) {
        assertThat(response).isNotBlank();
        assertThat(response).containsIgnoringCase("Croatia");
        assertThat(response).containsIgnoringCase("flight");
    }
    private String getClientResponseString(ChatClient chatClientWithoutToolsSearch) {
        return chatClientWithoutToolsSearch.prompt()
          .user("""
                  Find available flights from Romania to Croatia next week.
                  """)
          .call()
          .content();
    }
}

We’ve called our travel advisor clients with the same prompt and obtained the same verified results. Now, let’s compare the token usage in both of them:

[2026-05-24 11:39:07] [INFO] [c.b.s.t.TokenCounterAdvisor] - Total tokens spent: 974 //With tools search tool
[2026-05-24 11:39:10] [INFO] [c.b.s.t.TokenCounterAdvisor] - Total tokens spent: 3685 //Without tools search tool

As we can see, the difference in token usage is crucial. The more tools we have in our system, the greater the token savings the Tool Search Tool will provide.

4. Conclusion

In this article, we reviewed the Tool Search Tool and demonstrated how it helps reduce token usage in real scenarios. Using it, we can build large AI-integrated systems with hundreds of attached tools and use them efficiently, without wasting tokens. Additionally, we can explore other tool search strategies, such as vector search, or even build our own custom strategy to make tool discovery even more efficient.

As always, the code is available over on GitHub.