wip

Author: mariofusco

_posts/2025-05-08-agentic-ai-with-quarkus-p3.adoc

@@ -41,7 +41,9 @@ image::routing-workflow-trace.png[align=center, alt="Tracing routing workflow pa
 In this example, we use the same model to classify and generate the response. However, it is possible to use specialized models for the classification and each experts.
 Now, let’s see how we can transform this workflow approach into a more agentic one.
 
-Quarkus integration makes it straightforward to turn these AI “expert” services into _tools_ that another AI service can invoke. You only need to annotate the AI service methods with `@Tool` and configure the caller AI service with `@Toolbox`. This approach preserves the possibility of also invoking the single expert directly as an independent LLM service, and also using specialized models for each expert.
+Quarkus integration makes it straightforward to turn these AI “expert” services into _tools_ that another AI service can invoke. You only need to annotate the AI service methods with `@Tool` and configure the caller AI service with `@Toolbox`. This approach preserves the possibility of also invoking the single expert directly as an independent LLM service, and also using specialized models for each expert. Note that this `@Tool` annotation is not related with the presence of any MCP server and has the purpose of exposing the AI service also as a tool for other AI services. It is planned to discuss MCP in the next blog post of this series.
+
+is used to specify the name of the tool, which will be used by the agent to invoke it. The name of the tool is not necessarily the same as the name of the class, but it is a good practice to keep them aligned.
 
 [source, java]
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