refactor(mcp): update to use Spring Boot autoconfiguration and rename tool callbacks

Update MCP examples to use Spring Boot autoconfiguration instead of manual configuration.
Replace deprecated defaultTools method with defaultToolCallbacks across all examples.
Update documentation to reflect these changes and provide more detailed configuration examples.

- Update API from defaultTools to defaultToolCallbacks in all MCP examples
- Update README files with more detailed configuration examples

Signed-off-by: Christian Tzolov <christian.tzolov@broadcom.com>

model-context-protocol/sampling/mcp-sampling-client/README.md

@@ -10,6 +10,7 @@ The MCP Sampling Client:
 - Integrates with both OpenAI and Anthropic models
 - Demonstrates how to use model hints to select the appropriate LLM
 - Combines creative responses from multiple LLMs into a single result
+- Provides logging capabilities for MCP operations
 
 ## MCP Sampling Implementation
 
@@ -20,8 +21,13 @@ MCP Sampling is a powerful capability that allows an MCP server to delegate cert
 ```java
 @Bean
 McpSyncClientCustomizer samplingCustomizer(Map<String, ChatClient> chatClients) {
-    return (name, spec) -> {
-        spec.sampling(llmRequest -> {
+    return (name, mcpClientSpec) -> {
+        
+        mcpClientSpec = mcpClientSpec.loggingConsumer(logingMessage -> {            
+            System.out.println("MCP LOGGING: [" + logingMessage.level() + "] " + logingMessage.data());            
+        });
+
+        mcpClientSpec.sampling(llmRequest -> {
             var userPrompt = ((McpSchema.TextContent) llmRequest.messages().get(0).content()).text();
             String modelHint = llmRequest.modelPreferences().hints().get(0).name();
 
@@ -39,6 +45,7 @@ McpSyncClientCustomizer samplingCustomizer(Map<String, ChatClient> chatClients)
 
             return CreateMessageResult.builder().content(new McpSchema.TextContent(response)).build();
         });
+        System.out.println("Customizing " + name);
     };
 }
 ```
@@ -61,11 +68,10 @@ public Map<String, ChatClient> chatClients(List<ChatModel> chatModels) {
 4. **Integration with Spring AI**: The client leverages Spring AI's auto-configuration to set up the necessary components:
 
 ```java
-var mcpToolProvider = new SyncMcpToolCallbackProvider(
-    mcpClientsProvider.stream().flatMap(List::stream).toList());
+var mcpToolProvider = new SyncMcpToolCallbackProvider(mcpClients);
 
 ChatClient chatClient = ChatClient.builder(openAiChatModel)
-    .defaultTools(mcpToolProvider)
+    .defaultToolCallbacks(mcpToolProvider)
     .build();
 ```
 
@@ -113,6 +119,9 @@ spring.ai.mcp.client.sse.connections.server1.url=http://localhost:8080
 # Logging configuration
 logging.level.io.modelcontextprotocol.client=WARN
 logging.level.io.modelcontextprotocol.spec=WARN
+
+# Uncomment to disable MCP tool callbacks
+# spring.ai.mcp.client.toolcallback.enabled=false
 ```
 
 ## How It Works
@@ -161,8 +170,8 @@ When you run the application, you'll see output similar to:
 
 ```
 > USER: What is the weather in Amsterdam right now?
-Please incorporate all creative responses from all LLM providers.
-After the other providers add a poem that synthesizes the the poems from all the other providers.
+Please incorporate all createive responses from all LLM providers.
+After the other providers add a poem that synthesizes the poems from all the other providers.
 
 > ASSISTANT: I checked the current weather in Amsterdam for you. Here are the creative responses from different AI providers:
 

model-context-protocol/sampling/mcp-sampling-client/src/main/java/org/springframework/ai/mcp/samples/client/McpClientApplication.java

@@ -49,7 +49,7 @@ public class McpClientApplication {
 
 			var mcpToolProvider = new SyncMcpToolCallbackProvider(mcpClients);
 
-			ChatClient chatClient = ChatClient.builder(openAiChatModel).defaultTools(mcpToolProvider).build();
+			ChatClient chatClient = ChatClient.builder(openAiChatModel).defaultToolCallbacks(mcpToolProvider).build();
 
 			String userQuestion = """
 					What is the weather in Amsterdam right now?