refactor(mcp): update SQLite examples to use new MCP client API

- Replace deprecated StdioServerTransport with StdioClientTransport
- Update SyncMcpToolCallbackProvider to use builder pattern with noPrefix generator
- Change SQLite MCP-Server documentation links from GitHub to PyPI
- Remove obsolete function callback documentation sections
- Streamline MCP client initialization with new timeout configuration

These changes align with the latest Spring AI MCP integration patterns
and improve code maintainability across both simple and chatbot examples.

Signed-off-by: 虫洞 <[email protected]>

Author: 虫洞

model-context-protocol/sqlite/chatbot/README.md

@@ -2,7 +2,7 @@
 
 A demo application showcasing the integration of Spring AI with SQLite databases using the Model Context Protocol (MCP). This application enables natural language interactions with your SQLite database through a command-line interface.
 
-It uses the [SQLite MCP-Server](https://github.com/modelcontextprotocol/servers/tree/main/src/sqlite) to enable running SQL queries, analyzing business data, and automatically generating business insight memos.
+It uses the [SQLite MCP-Server](https://pypi.org/project/mcp-server-sqlite/) to enable running SQL queries, analyzing business data, and automatically generating business insight memos.
 
 The demo starts a simple chatbot where your can ask qustions about the data  stored in the database.
 
@@ -68,7 +68,7 @@ It has a `PRODUCTS` table and was created using the script `create-database.sh`
 Enables real-time conversation with your database:
 
 ```bash
-./mvnw spring-boot:run -Dspring-boot.run.profiles=chat
+./mvnw spring-boot:run
 ```
 
 ## Architecture Overview
@@ -85,8 +85,8 @@ public McpSyncClient mcpClient() {
             .args("mcp-server-sqlite", "--db-path", getDbPath())
             .build();
 
-    var mcpClient = McpClient.sync(new StdioServerTransport(stdioParams),
-            Duration.ofSeconds(10), new ObjectMapper());
+   var mcpClient = McpClient.sync(new StdioClientTransport(stdioParams, McpJsonMapper.createDefault()))
+           .requestTimeout(Duration.ofSeconds(10)).build();
 
     var init = mcpClient.initialize();
 
@@ -105,39 +105,6 @@ This configuration:
 
 The `destroyMethod = "close"` annotation ensures proper cleanup when the application shuts down.
 
-### Function Callbacks
-
-The application registers MCP tools with Spring AI using function callbacks:
-
-```java
-@Bean
-public List<McpFunctionCallback> functionCallbacks(McpSyncClient mcpClient) {
-    return mcpClient.listTools(null)
-            .tools()
-            .stream()
-            .map(tool -> new McpFunctionCallback(mcpClient, tool))
-            .toList();
-}
-```
-
-#### Purpose
-
-This bean is responsible for:
-1. Discovering available MCP tools from the client
-2. Converting each tool into a Spring AI function callback
-3. Making these callbacks available for use with the ChatClient
-
-
-#### How It Works
-
-1. `mcpClient.listTools(null)` queries the MCP server for all available tools
-    - The `null` parameter represents a pagination cursor
-    - When null, returns the first page of results
-    - A cursor string can be provided to get results after that position
-2. `.tools()` extracts the tool list from the response
-3. Each tool is transformed into a `McpFunctionCallback` using `.map()`
-4. These callbacks are collected into an array using `.toArray(McpFunctionCallback[]::new)`
-
 #### Usage
 
 The registered callbacks enable the ChatClient to:

model-context-protocol/sqlite/chatbot/src/main/java/org/springframework/ai/mcp/samples/sqlite/Application.java

@@ -14,6 +14,7 @@
 import org.springframework.ai.chat.client.ChatClient;
 import org.springframework.ai.chat.client.advisor.MessageChatMemoryAdvisor;
 import org.springframework.ai.chat.memory.MessageWindowChatMemory;
+import org.springframework.ai.mcp.McpToolNamePrefixGenerator;
 import org.springframework.ai.mcp.SyncMcpToolCallbackProvider;
 import org.springframework.boot.CommandLineRunner;
 import org.springframework.boot.SpringApplication;
@@ -33,9 +34,11 @@ public CommandLineRunner interactiveChat(ChatClient.Builder chatClientBuilder,
 			List<McpSyncClient> mcpClients,
 			ConfigurableApplicationContext context) {
 		return args -> {
-
-			var chatClient = chatClientBuilder
-					.defaultToolCallbacks(new SyncMcpToolCallbackProvider(mcpClients))
+			var toolCallbackProvider = SyncMcpToolCallbackProvider.builder()
+				.mcpClients(mcpClients)
+				.toolNamePrefixGenerator(McpToolNamePrefixGenerator.noPrefix())
+				.build();
+			var chatClient = chatClientBuilder.defaultToolCallbacks(toolCallbackProvider)
 					.defaultAdvisors(MessageChatMemoryAdvisor.builder(MessageWindowChatMemory.builder().build()).build())
 					.build();
 

model-context-protocol/sqlite/simple/README.md

@@ -2,7 +2,7 @@
 
 A demo application showcasing the integration of Spring AI with SQLite databases using the Model Context Protocol (MCP). This application enables natural language interactions with your SQLite database through a command-line interface.
 
-It uses the [SQLite MCP-Server](https://github.com/modelcontextprotocol/servers/tree/main/src/sqlite) to enable running SQL queries, analyzing business data, and automatically generating business insight memos.
+It uses the [SQLite MCP-Server](https://pypi.org/project/mcp-server-sqlite/) to enable running SQL queries, analyzing business data, and automatically generating business insight memos.
 
 ## Features
 
@@ -67,13 +67,15 @@ The bean definitions are described below, starting with the `ChatClient`
 
 ```java
 @Bean
-@Profile("!chat")
 public CommandLineRunner predefinedQuestions(ChatClient.Builder chatClientBuilder,
-                                           List<McpFunctionCallback> functionCallbacks,
-                                           ConfigurableApplicationContext context) {
-    return args -> {
-        var chatClient = chatClientBuilder.defaultFunctions(functionCallbacks.toArray(new McpFunctionCallback[0]))
-                .build();
+        List<McpSyncClient> mcpClients, ConfigurableApplicationContext context) {
+
+      return args -> {
+         var toolCallbackProvider = SyncMcpToolCallbackProvider.builder()
+                 .mcpClients(mcpClients)
+                 .toolNamePrefixGenerator(McpToolNamePrefixGenerator.noPrefix())
+                 .build();
+         var chatClient = chatClientBuilder.defaultToolCallbacks(toolCallbackProvider).build();
          // Run Predefined Questions
          System.out.println(chatClient.prompt(
             "Can you connect to my SQLite database and tell me what products are available, and their prices?").call().content());
@@ -82,51 +84,10 @@ public CommandLineRunner predefinedQuestions(ChatClient.Builder chatClientBuilde
 }
 ```
 
-The chat client setup is remarkably simple - it just needs the function callbacks that were automatically created from the MCP tools. Spring's dependency injection handles all the wiring, making the integration seamless.
+The chat client setup is remarkably simple - it just needs the mcp clients. Spring's dependency injection handles all the wiring, making the integration seamless.
 
 Now let's look at the other bean definitions in detail...
 
-### Function Callbacks
-
-The application registers MCP tools with Spring AI using function callbacks:
-
-```java
-@Bean
-public List<McpFunctionCallback> functionCallbacks(McpSyncClient mcpClient) {
-    return mcpClient.listTools(null)
-            .tools()
-            .stream()
-            .map(tool -> new McpFunctionCallback(mcpClient, tool))
-            .toList();
-}
-```
-
-#### Purpose
-
-This bean is responsible for:
-1. Discovering available MCP tools from the client
-2. Converting each tool into a Spring AI function callback
-3. Making these callbacks available for use with the ChatClient
-
-
-#### How It Works
-
-1. `mcpClient.listTools(null)` queries the MCP server for all available tools
-   - The `null` parameter represents a pagination cursor
-   - When null, returns the first page of results
-   - A cursor string can be provided to get results after that position
-2. `.tools()` extracts the tool list from the response
-3. Each tool is transformed into a `McpFunctionCallback` using `.map()`
-4. These callbacks are collected into an array using `.toArray(McpFunctionCallback[]::new)`
-
-#### Usage
-
-The registered callbacks enable the ChatClient to:
-- Access MCP tools during conversations
-- Handle function calls requested by the AI model
-- Execute tools against the MCP server (e.g., SQLite database)
-
-
 ### MCP Client 
 
 The application uses a synchronous MCP client to communicate with the SQLite database:
@@ -138,8 +99,8 @@ public McpSyncClient mcpClient() {
             .args("mcp-server-sqlite", "--db-path", getDbPath())
             .build();
 
-    var mcpClient = McpClient.sync(new StdioServerTransport(stdioParams),
-            Duration.ofSeconds(10), new ObjectMapper());
+   var mcpClient = McpClient.sync(new StdioClientTransport(stdioParams, McpJsonMapper.createDefault()))
+           .requestTimeout(Duration.ofSeconds(10)).build();
 
     var init = mcpClient.initialize();
     System.out.println("MCP Initialized: " + init);

model-context-protocol/sqlite/simple/src/main/java/org/springframework/ai/mcp/samples/sqlite/Application.java

@@ -11,6 +11,7 @@
 import io.modelcontextprotocol.json.McpJsonMapper;
 
 import org.springframework.ai.chat.client.ChatClient;
+import org.springframework.ai.mcp.McpToolNamePrefixGenerator;
 import org.springframework.ai.mcp.SyncMcpToolCallbackProvider;
 import org.springframework.boot.CommandLineRunner;
 import org.springframework.boot.SpringApplication;
@@ -30,9 +31,11 @@ public CommandLineRunner predefinedQuestions(ChatClient.Builder chatClientBuilde
 			List<McpSyncClient> mcpClients, ConfigurableApplicationContext context) {
 
 		return args -> {
-			var chatClient = chatClientBuilder
-					.defaultToolCallbacks(new SyncMcpToolCallbackProvider(mcpClients))
-					.build();
+			var toolCallbackProvider = SyncMcpToolCallbackProvider.builder()
+				.mcpClients(mcpClients)
+				.toolNamePrefixGenerator(McpToolNamePrefixGenerator.noPrefix())
+				.build();
+			var chatClient = chatClientBuilder.defaultToolCallbacks(toolCallbackProvider).build();
 			System.out.println("Running predefined questions with AI model responses:\n");
 
 			// Question 1