Can You Build an MCP Server with Spring Boot? Complete Java Guide to Standardized AI APIs

Why MCP is becoming a key part of AI architecture

A typical scenario: an internal AI assistant can query calendars and Slack, and a product manager asks whether the AI can directly query transaction records. The choice of integration method determines portability and future‑proofing.

Three implementation options

REST tool : expose a custom REST endpoint and define a tool that calls it.

Pros : fast implementation (can be ready within two days).

Cons : tightly bound to a specific AI client; low portability.

Platform plugin : develop a plugin for a particular AI platform (e.g., ChatGPT Plugin).

Pros : good integration experience.

Cons : strong platform lock‑in; switching platforms requires rewriting.

MCP protocol (recommended) : expose services via the Model Context Protocol so any MCP‑compatible client can invoke them.

Pros : standardized interface, reusable across multiple AI clients, and aligned with the evolving AI ecosystem.

The essence of MCP

MCP is not merely an API; it is a protocol that lets an LLM understand a system’s capabilities. It consists of three parts:

Tools – definition of the capability.

Resources – access to underlying data.

Prompts – templates for interaction.

Building an MCP server with Spring Boot (Java practical)

Project structure

/home/project/mcp-server/
├── src/main/java/com/icoderoad/mcp/
│   ├── controller/
│   ├── service/
│   ├── tool/
│   └── config/
├── src/main/resources/
│   └── application.yml
└── pom.xml

Maven dependencies

<dependencies>
    <!-- Spring Boot Web -->
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>

    <!-- Jackson -->
    <dependency>
        <groupId>com.fasterxml.jackson.core</groupId>
        <artifactId>jackson-databind</artifactId>
    </dependency>

    <!-- Lombok (optional) -->
    <dependency>
        <groupId>org.projectlombok</groupId>
        <artifactId>lombok</artifactId>
    </dependency>
</dependencies>

Defining an MCP tool interface

package com.icoderoad.mcp.tool;

import java.util.Map;

/** MCP tool definition interface */
public interface McpTool {
    /** Tool name */
    String name();

    /** Tool description */
    String description();

    /** Execution logic */
    Object execute(Map<String, Object> input);
}

Implementing a transaction‑lookup tool

package com.icoderoad.mcp.tool;

import org.springframework.stereotype.Component;
import java.util.HashMap;
import java.util.Map;

/** Simulated transaction lookup tool */
@Component
public class TransactionLookupTool implements McpTool {
    @Override
    public String name() { return "transaction_lookup"; }

    @Override
    public String description() { return "根据用户ID查询交易记录"; }

    @Override
    public Object execute(Map<String, Object> input) {
        String userId = (String) input.get("userId");
        Map<String, Object> result = new HashMap<>();
        result.put("userId", userId);
        result.put("amount", 1280.55);
        result.put("status", "SUCCESS");
        return result;
    }
}

Tool registry service

package com.icoderoad.mcp.service;

import com.icoderoad.mcp.tool.McpTool;
import org.springframework.stereotype.Service;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

/** Tool registration and management */
@Service
public class ToolRegistry {
    private final Map<String, McpTool> toolMap = new HashMap<>();

    public ToolRegistry(List<McpTool> tools) {
        for (McpTool tool : tools) {
            toolMap.put(tool.name(), tool);
        }
    }

    public McpTool getTool(String name) { return toolMap.get(name); }
}

MCP request entry controller

package com.icoderoad.mcp.controller;

import com.icoderoad.mcp.service.ToolRegistry;
import com.icoderoad.mcp.tool.McpTool;
import org.springframework.web.bind.annotation.*;
import java.util.Map;

/** MCP request entry */
@RestController
@RequestMapping("/mcp")
public class McpController {
    private final ToolRegistry toolRegistry;

    public McpController(ToolRegistry toolRegistry) { this.toolRegistry = toolRegistry; }

    @PostMapping("/invoke")
    public Object invoke(@RequestBody Map<String, Object> request) {
        String toolName = (String) request.get("tool");
        Map<String, Object> input = (Map<String, Object>) request.get("input");
        McpTool tool = toolRegistry.getTool(toolName);
        if (tool == null) {
            throw new RuntimeException("Tool not found: " + toolName);
        }
        return tool.execute(input);
    }
}

Full‑chain MCP call flow

Integrating LLMs: making the AI actually use the service

The LLM must receive a JSON tool description. Example:

{
  "name": "transaction_lookup",
  "description": "查询用户交易记录",
  "input_schema": {
    "type": "object",
    "properties": {"userId": {"type": "string"}},
    "required": ["userId"]
  }
}

Production‑level optimizations

Automatic tool discovery via Spring component scanning.

Security configuration (token‑based) in application.yml:

mcp:
  security:
    enabled: true
    token: your-secret-token

Global exception handling with @RestControllerAdvice:

@RestControllerAdvice
public class GlobalExceptionHandler {
    @ExceptionHandler(Exception.class)
    public Map<String, Object> handle(Exception e) {
        return Map.of("error", e.getMessage());
    }
}

Rate limiting and monitoring suggestions: gateway throttling, Prometheus + Grafana, audit logging.

Architectural comparison (REST tool vs Plugin vs MCP)

REST tool

Development cost: low

Scalability: poor

Binding degree: high (tied to a specific client)

Plugin

Development cost: medium

Scalability: average

Binding degree: very high (platform specific)

MCP

Development cost: medium

Scalability: very high

Binding degree: very low (standardized across clients)

Conclusion

When AI evolves from a conversational tool to a system entry point, interface design becomes an ecosystem concern. REST solves “can use”, Plugin solves “easy to use”, while MCP ensures that a service can be invoked by any future AI system, making Java‑based services future‑proof in an AI‑driven landscape.