• 02 Oct, 2025
• by krunal thakkar

Supercharging AI Integrations: Building an MCP Server with Spring AI

AI adoption is no longer about just plugging into a single model — it’s about connecting AI agents to real-world tools, workflows, and data sources in a seamless and secure way. That’s where the Model Context Protocol (MCP) comes in. And when you combine MCP with Spring AI, you get a powerful foundation to supercharge your AI integrations — turning your Spring Boot apps into AI-ready microservices.

🔎 What is MCP?

The Model Context Protocol (MCP) is a new standard that enables tools, APIs, and data sources to communicate directly with AI models and agentic systems.

Instead of writing custom adapters for every integration, MCP provides a unified, secure, and scalable way to:

• 🔗 Expose business APIs and tools to AI systems
• ⚡ Enable agents to query knowledge bases dynamically
• 🔒 Maintain secure, auditable access to data
• 🛠️ Standardize how LLMs interact with external services

Think of it as “OAuth for AI tools” — a protocol that ensures models can interact with resources without breaking enterprise rules.

🌱 Why Pair MCP with Spring AI?

Spring has been the backbone of enterprise development for decades. With the Spring AI project, developers can now integrate LLMs into existing architectures while leveraging Spring’s proven ecosystem.

Here’s why Spring AI is the perfect partner for MCP:

• ✅ Plug-and-play AI: Built-in support for OpenAI, Anthropic, HuggingFace, and more
• ✅ Spring Boot simplicity: Build MCP servers as easily as REST APIs
• ✅ Enterprise-grade reliability: Leverage Spring Security, Observability, and Spring Data
• ✅ Future-ready integrations: Use MCP to unlock agent-driven workflows securely

By building your MCP server with Spring AI, you’re not just experimenting with AI — you’re productionizing it.

🛠️ How to Build Your Own MCP Server with Spring AI

Here’s a step-by-step blueprint:

1. Set up Spring Boot + Spring AI

Add Spring AI dependencies in your pom.xml:

                                
                                    <dependency>
                                        <groupId>org.springframework.ai</groupId>
                                        <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
                                        <version>0.8.0</version>
                                    </dependency>
                                
                            

2. Expose MCP Endpoints

Your MCP server is just a Spring Boot app with endpoints:

                                
                                    @RestController
                                    @RequestMapping("/mcp")
                                    public class MCPController {
                                        @GetMapping("/health")
                                        public String health() {
                                            return "✅ MCP Server is running";
                                        }

                                        @PostMapping("/query")
                                        public ResponseEntity<String> handleQuery(@RequestBody QueryRequest request) {
                                            return ResponseEntity.ok("Received: " + request.getPrompt());
                                        }
                                    }
                                
                            

3. Connect MCP to Spring AI

Integrate with an LLM via Spring AI’s ChatClient:

                                
                                    @Autowired
                                    private ChatClient chatClient;

                                    @PostMapping("/query")
                                    public ResponseEntity<String> handleQuery(@RequestBody QueryRequest request) {
                                        ChatResponse response = chatClient.call(request.getPrompt());
                                        return ResponseEntity.ok(response.getResult().getOutput().getContent());
                                    }
                                
                            

4. Secure & Deploy
• Use Spring Security for controlled access
• Dockerize and deploy to Kubernetes or Cloud
• Add monitoring with Spring Boot Actuator

Now your Spring Boot service is not just an API — it’s an MCP-compliant server ready for AI agents.

🌍 Real-World Use Cases

Here’s how enterprises can supercharge AI integrations with an MCP Server:

• 🏦 Finance: AI copilots securely accessing transaction APIs
• 🏥 Healthcare: AI agents fetching patient insights (HIPAA-compliant)
• 🛒 E-commerce: Intelligent assistants querying product catalogs & logistics
• 📚 Knowledge Management: Enterprise LLMs retrieving structured/unstructured data

Final Thoughts

The Model Context Protocol is paving the way for AI-native integrations. By pairing it with Spring AI, developers can move beyond prototypes and deliver enterprise-grade AI systems.

If you’re already in the Spring ecosystem, this is your opportunity to supercharge your apps with AI integrations — and stay ahead of the curve in the AI-driven future.