Spring AI Example
Project Overview
This project demonstrates various implementation patterns and best practices for using Spring AI tools. It consists of two main modules:
- mcp-server : Implements the Model Context Protocol (MCP) server with both WebFlux and WebMvc SSE support
- proposal-agent : Implements the MCP client for making AI-powered proposals
Project Structure
.
├── mcp-server/ # MCP Server implementation
│ ├── src/ # Server source code
│ └── README.md # Server documentation
├── proposal-agent/ # MCP Client implementation
│ ├── src/ # Client source code
│ └── README.md # Client documentation
└── src/ # Common source code
Tools Implementation Patterns
Methods as Tools
Spring AI supports using methods as tools by annotating them with @Tool. Example from DateTimeTools:
@Tool(name = "getCurrentDateTime", description = "Get the current date and time")
public String getCurrentDateTime() {
return LocalDateTime.now().atZone(LocaleContextHolder.getTimeZone().toZoneId()).toString();
}
Tool Result Converter
Custom result converters can be implemented to control how tool results are formatted. Example from CustomToolCallResultConverter:
@Tool(name = "getCustomer",
description = "Retrieve customer information",
resultConverter = CustomToolCallResultConverter.class)
public Customer getCustomer(String name, ToolContext context) {
return new Customer(name, "[[email protected]](/cdn-cgi/l/email-protection)");
}
Tool Context
Spring AI provides a ToolContext parameter that can be injected into tool methods to access contextual information:
public Customer getCustomerByEmail(String email, ToolContext context) {
log.info("Context: {}", context);
return new Customer("Demo", email);
}
Tool Parameters
Tool parameters can be annotated with @ToolParam to provide descriptions:
@Tool(name = "setAlarm")
public void setAlarm(@ToolParam(description = "Time in ISO-8601 format") String time) {
// Implementation
}
Configuration
The project uses Spring Boot with the following key configurations:
- Ollama AI model integration
- Vector store with PGVector
- H2 database for development
- CORS configuration for web access
- MCP Server implementation
- MCP Client implementation
- SSE implementation
SSE implementation
an implementation of SSE (Server-Sent Events) for real-time updates. This is achieved by using the SseEmitter class.
Technology Stack
- Spring Boot
- Spring AI
- Ollama AI Model
- PGVector Vector Store
- H2 Database
- CORS Configuration
- SSE (Server-Sent Events) for real-time updates
