The Model Context Protocol (MCP) is a powerful feature in Spring AI that enables AI models to access external tools and resources through a standardized interface. One interesting capabilities of MCP is its ability to dynamically update available tools at runtime.

This blog post explores how Spring AI implements dynamic tool updates in MCP, providing flexibility and extensibility to AI-powered applications.

The related example code is available here: Dynamic Tool Update Example

Understanding the Model Context Protocol

Before diving into dynamic tool updates, let's understand what MCP is and…

This blog post demonstrates practical implementations of Prompt Engineering techniques using Spring AI.

The examples and patterns in this article are based on the comprehensive Prompt Engineering Guide that covers the theory, principles, and patterns of effective prompt engineering.

The blog shows how to translate those concepts into working Java code using Spring AI's fluent ChatClient API.

For convenience, the examples are structured to follow the same patterns and techniques outlined in the original guide.

The demo source code used in this article is available at: https://github.com/spring…

We're pleased to announce Spring AI MCP 0.6.0 milestone release.

Breaking Changes ⚠️

• McpClient.using() - Use McpClient.sync() or McpClient.async() instead
• McpServer.using() - Use McpServer.sync() or McpServer.async() instead
• McpServer.ToolRegistration - Use McpServerFeatures.SyncToolRegistration or McpServerFeatures.AsyncToolRegistration instead
• McpServer.ResourceRegistration - Use McpServerFeatures.SyncResourceRegistration or McpServerFeatures.AsyncResourceRegistration instead
• McpServer.PromptRegistration - Use McpServerFeatures.SyncPromptRegistration or McpServerFeatures.AsyncPromptRegistration instead
• ToolHelper.toToolRegistration() - Use ToolHelper.toSyncToolRegistration()…

In a recent research publication: Building effective agents, Anthropic shared valuable insights about building effective Large Language Model (LLM) agents. What makes this research particularly interesting is its emphasis on simplicity and composability over complex frameworks. Let's explore how these principles translate into practical implementations using Spring AI.

While the pattern descriptions and diagrams are sourced from Anthropic's original publication, we'll focus on how to implement these patterns using Spring AI's features for model portability and structured output. We recommend…

We're pleased to announce Spring AI MCP 0.5.0 milestone release.

Major Features & Improvements

Transport Layer Enhancements

• New Servlet-based SSE Transport

  • Added HttpServletSseServerTransport with Servlet 6.0 support
  • Enables integration with any Java HTTP server supporting Servlets
  • Compatible with Jakarta Servlet API 6.1.0
  • Includes comprehensive integration tests with Tomcat

• Enhanced WebMVC Transport

  • Replaced Spring's SseEmitter with custom BlockingQueue-based implementation
  • Improved event delivery control and connection management
  • Added dedicated session management with SSEEvent record
  • Enhanced error handling and timeout management
  • Includes comprehensive integration tests
…

We're pleased to announce Spring AI MCP 0.4.0 milestone release.

Repository Configuration

Add this Spring milestone repository to your POM:

<repositories>
  <repository>
    <id>spring-milestones</id>
    <name>Spring Milestones</name>
    <url>https://repo.spring.io/libs-milestone-local</url>
    <snapshots>
      <enabled>false</enabled>
    </snapshots>
  </repository>
</repositories>

Major Features

Reference documentation: https://docs.spring.io/spring-ai-mcp/reference/overview.html

Enhanced Roots Management

• Implemented proper ListRootsResult wrapping in async client
• Added listRoots methods in server components
• Implemented roots change notification handling in async server
• Added roots integration tests including async notifications
• Added support for roots change notification with single and multiple consumers
• Improved robustness of root addition/removal scenarios
…

We're pleased to announce Spring AI MCP 0.2.0 milestone release.

Repository Configuration

Add this Spring milestone repository to your POM:

<repositories>
  <repository>
    <id>spring-milestones</id>
    <name>Spring Milestones</name>
    <url>https://repo.spring.io/libs-milestone-local</url>
    <snapshots>
      <enabled>false</enabled>
    </snapshots>
  </repository>
</repositories>

Major Features

MCP Server Enhancements

• Introduced new McpServer factory with builder pattern for flexible configuration
• Added McpAsyncServer with non-blocking operations and reactive support
• Implemented McpSyncServer as synchronous wrapper around async implementation
• Added runtime tool management capabilities
• Introduced server capabilities and implementation information support
…

We're pleased to announce Spring AI MCP 0.2.0 milestone release.

Repository Configuration

Add this Spring milestone repository to your POM:

<repositories>
  <repository>
    <id>spring-milestones</id>
    <name>Spring Milestones</name>
    <url>https://repo.spring.io/libs-milestone-local</url>
    <snapshots>
      <enabled>false</enabled>
    </snapshots>
  </repository>
</repositories>

Breaking Changes

• Module restructuring (see the Module Names Updated section below)
• Renamed StdioServerTransport to StdioClientTransport

Key Features

API Updates

• Simplified McpClient listing operations (no cursor parameter needed)
• Added McpClient.Builder support.
…

We're excited to introduce Spring AI MCP, a robust Java SDK implementation of the Model Context Protocol (MCP). This new addition to the Spring AI ecosystem brings standardized AI model integration capabilities to the Java platform.

What is MCP?

The Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to Large Language Models (LLMs). MCP provides a standardized way to connect AI models to different data sources and tools, making integration seamless and consistent. It helps you build agents and complex workflows on top of LLMs. LLMs frequently…

The Amazon Bedrock Nova models represent a new generation of foundation models supporting a broad range of use cases, from text and image understanding to video-to-text analysis.

With the Spring AI Bedrock Converse API integration, developers can seamlessly connect to these advanced Nova models and build sophisticated conversational applications with minimal effort.

This blog post introduces the key features of Amazon Nova models, demonstrates their integration with Spring AI's Bedrock Converse API, and provides practical examples for text, image, video, document processing, and function…