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…

This blog post is co-authored by our great contributor Thomas Vitale.

OpenAI provides specialized models for speech-to-text and text-to-speech conversion, recognized for their performance and cost-efficiency. Spring AI integrates these capabilities via Voice-to-Text and Text-to-Speech (TTS).

The new Audio Generation feature (gpt-4o-audio-preview) goes further, enabling mixed input and output modalities. Audio inputs can contain richer data than text alone. Audio can convey nuanced information like tone and inflection, and together with the audio outputs it enables asynchronous speech-to-speech interactions. Additionally, this new multimodality opens up possibilities for innovative applications, such as structured data extraction. Developers can extract structured information not just from simple text, but also from images and audio, building complex, structured objects…

This blog post is co-authored by our great contributor Thomas Vitale.

Ollama now supports all GGUF models from Hugging Face, allowing access to over 45,000 community-created models through Spring AI's Ollama integration, runnable locally.

We'll explore using this new feature with Spring AI. The Spring AI Ollama integration can automatically pull unavailable models for both chat completion and embedding models. This is useful when switching models or deploying to new environments.

Setting Up Spring AI with Ollama

Install Ollama on your system: https://ollama.com/download.

Tip: Spring AI also supports running Ollama via Testcontainers or integrating with an external Ollama service via Kubernetes Service Bindings…