Spring Ai Example
What is MCP Server?
MCP Server is an AI-powered backend service that facilitates context-aware interactions between applications and AI models. It supports both reactive (WebFlux) and traditional (WebMvc) programming models with Server-Sent Events (SSE) for real-time updates.How to use MCP Server?
Integrate with MCP Server by connecting your client application to its API endpoints. The server provides AI tool integration, context management, and real-time event streaming capabilities.Use Cases
Ideal for building AI-powered applications that require: real-time data processing, context-aware responses, AI tool integration, and event-driven architectures.Key Features
AI Tool IntegrationSeamlessly integrate AI tools through annotated methods with automatic parameter handling
Context ManagementMaintain conversation context across multiple interactions with the ToolContext parameter
Real-time UpdatesBuilt-in SSE support for streaming AI responses and progress updates
Flexible Programming ModelsSupport for both reactive (WebFlux) and traditional (WebMvc) implementations
Custom Result FormattingControl how tool results are presented through custom converters
Pros and Cons
Advantages
Simplifies AI integration through annotation-based tool definitions
Maintains conversation context for more natural interactions
Provides real-time feedback through SSE streams
Supports both reactive and traditional programming styles
Customizable output formatting for different client needs
Limitations
Requires Spring ecosystem knowledge for full customization
SSE implementation may have browser compatibility considerations
AI tool performance depends on underlying model capabilities
Context management adds complexity to simple use cases
Getting Started
Define AI Tools
Create Java methods with @Tool annotation to expose functionality
Configure Server
Set up your Spring Boot application with required dependencies
Connect Client
Use the proposal-agent module or create your own client to call MCP endpoints
Handle Responses
Process tool results and SSE events in your client application
Example Scenarios
Customer Information RetrievalGet customer details using natural language queries
Real-time Data ProcessingStream processing results as they become available
Contextual ConversationMaintain context across multiple interactions
Frequently Asked Questions
What's the difference between WebFlux and WebMvc implementations?
How do I add custom AI tools?
Can I use MCP Server without Spring Boot?
How is conversation context maintained?
Additional Resources
Spring AI Documentation
Official Spring AI project documentation
SSE Specification
Server-Sent Events technical specification
Example Repository
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