Model Context Protocol with Spring AI, Building MCP Clients and Servers in Java

Model Context Protocol with Spring AI: Building MCP Clients and Servers in Java

In the previous article, we explored how to build AI agents with Spring AI using:

LLMs
 ↓
RAG
 ↓
Tool Calling
 ↓
Memory
 ↓
Agent Workflows

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Tool calling gives an AI application the ability to interact with external capabilities.

But another problem appears as AI systems become larger.

Imagine you have:

Customer Service Agent
        ↓
Order APIs
Payment APIs
CRM APIs
Knowledge Base
Email Service

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And another application has:

Sales Agent
        ↓
CRM
Calendar
Email
Customer Database

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And another has:

Developer Agent
        ↓
Git Repository
Issue Tracker
CI/CD
Documentation

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If every AI application implements every integration differently, the architecture quickly becomes difficult to maintain.

This is where Model Context Protocol (MCP) becomes interesting.

MCP provides a standardized way for AI applications to interact with external tools and resources. Spring AI provides support for both building MCP servers and consuming MCP servers from Spring Boot applications.

In this article, we’ll build a mental model for MCP and explore how Java developers can use it with Spring AI.

What Is MCP?

MCP stands for:

Model Context Protocol

At a high level, MCP standardizes how an AI application communicates with external capabilities such as:

Tools
Resources
Prompts

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Instead of every AI application inventing its own integration mechanism:

AI Application
 ↓
Custom Tool Integration
 ↓
CRM

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we can have:

AI Application
 ↓
MCP Client
 ↓
MCP Protocol
 ↓
MCP Server
 ↓
CRM

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The MCP server exposes capabilities through a standardized interface.

The AI application doesn’t need to understand every internal implementation detail of the external system.

Why MCP Exists

Suppose you build an AI assistant that needs access to:

GitHub
Slack
PostgreSQL
Google Calendar
Internal APIs
File Systems

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Without a standard protocol, your application might contain:

GitHub Integration
Slack Integration
PostgreSQL Integration
Calendar Integration
Internal API Integration

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Each integration may have its own:

Authentication
Tool Schema
Request Format
Response Format
Connection Management
Error Handling

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Now imagine another AI application needs the same capabilities.

You may end up rebuilding many of the same integrations.

MCP addresses this by creating a common protocol for AI applications and external servers.

Conceptually:

                    AI Application
                         │
                    MCP Client
                         │
                MCP Protocol
                         │
       ┌─────────────────┼─────────────────┐
       ↓                 ↓                 ↓
  MCP Server         MCP Server         MCP Server
       ↓                 ↓                 ↓
     CRM              GitHub            Database

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This is one of the main ideas behind MCP.

MCP Is Not an LLM

This distinction is important.

MCP is not:

An AI model

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It is a protocol for connecting AI applications with capabilities.

Think of the stack like this:

LLM
 ↓
AI Application
 ↓
MCP Client
 ↓
MCP Protocol
 ↓
MCP Server
 ↓
Tools / Resources
 ↓
External System

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The LLM performs reasoning.

The MCP layer provides standardized communication.

The external system performs the actual operation.

MCP Client vs MCP Server

MCP introduces two important roles.

MCP Client

The MCP client lives inside the AI application.

Its responsibility is to connect to MCP servers and interact with the capabilities they expose.

For example:

Spring Boot AI Application
        ↓
MCP Client
        ↓
Weather MCP Server

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The client can discover and use the server’s available capabilities.

MCP Server

The MCP server exposes capabilities.

For example:

Weather MCP Server

Tools:
getWeather()
getForecast()

Resources:
weather://cities

Prompts:
weather-analysis

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The server is responsible for implementing those capabilities.

Spring AI provides Boot starters and APIs for both sides of this architecture.

The Basic MCP Architecture

A simplified architecture looks like:

                    User
                     ↓
                 Spring Boot
                     ↓
                  ChatClient
                     ↓
                  MCP Client
                     ↓
                MCP Protocol
                     ↓
                MCP Server
                     ↓
                   Tool
                     ↓
                External API

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For example:

User:
What's the weather in Paris?

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The AI application can discover a weather tool exposed by an MCP server.

The flow becomes:

User
 ↓
LLM
 ↓
MCP Tool
 ↓
Weather MCP Server
 ↓
Weather API
 ↓
Tool Result
 ↓
LLM
 ↓
Final Answer

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MCP and Traditional Tool Calling

At this point, you might ask:

“Isn’t this just tool calling?”

There is an important distinction.

Traditional Spring AI tool calling can expose application methods directly:

@Tool
public String getWeather(String city) {
    return weatherService.getWeather(city);
}

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Your application owns the tool.

With MCP:

AI Application
      ↓
MCP Client
      ↓
Remote MCP Server
      ↓
Tool

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The tool can live outside the application.

This creates a cleaner separation between:

AI Application

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and:

Capability Provider

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Spring AI integrates MCP tools into its tool-calling architecture, allowing applications to consume tools exposed by MCP servers.

MCP Tools

One of the most important MCP capabilities is the tool.

A tool represents an action that an AI application can invoke.

For example:

getWeather()
createTicket()
searchCustomers()
getOrder()
sendEmail()

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A weather server might expose:

getTemperature(city)

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A CRM server might expose:

findCustomer(email)
createLead(customer)
updateLead(leadId)

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A developer server might expose:

searchRepository(query)
getBuildStatus()
createIssue(title)

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The MCP client can discover these tools and make them available to the AI application.

MCP Resources

MCP is not limited to actions.

It can also expose resources.

A resource represents information that an MCP client can access.

For example:

customer://123
order://ORD-10291
file://README.md
database://schema

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Think of the distinction as:

Tool
=
Do something

Resource
=
Access something

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For example:

Tool:
createTicket()

Resource:
customer://123

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A server can expose both.

MCP Prompts

MCP also supports prompts.

A server can provide reusable prompt templates for specific tasks.

For example:

Prompt:
analyze-customer

Input:
customerId

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Or:

Prompt:
summarize-order

Input:
orderId

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This allows prompt templates to become part of the server-provided capabilities rather than being hardcoded independently in every client.

Spring AI’s MCP support includes annotations for tools, resources, and prompts.

Building an MCP Server with Spring AI

Let’s build a simple MCP server.

Imagine a weather service.

Our application already has:

@Service
public class WeatherService {

    public String getTemperature(String city) {
        return "22°C";
    }
}

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We can expose this capability through an MCP tool.

With Spring AI’s annotation-based MCP support:

@Service
public class WeatherTools {

    @McpTool(description = "Get the current temperature for a city")
    public String getTemperature(
            @McpToolParam(
                description = "City name",
                required = true
            )
            String city) {

        return weatherService.getTemperature(city);
    }
}

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The MCP annotation model allows Spring services to expose capabilities as MCP operations.

Creating the MCP Server

For a Spring Boot application, Spring AI provides MCP server starters.

For example:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-mcp-server-webmvc</artifactId>
</dependency>

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You can configure the server to use Streamable HTTP:

spring.ai.mcp.server.protocol=STREAMABLE

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Spring AI 2.x supports MCP server transports including Streamable HTTP, stateless Streamable HTTP, SSE, and STDIO. Streamable HTTP is the current recommended HTTP transport in Spring AI 2.x, while SSE is deprecated for this use case.

What Happens Inside the MCP Server?

Conceptually:

Spring Boot
     ↓
MCP Server
     ↓
Tool Registry
     ↓
@McpTool
     ↓
WeatherService
     ↓
Weather API

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The server exposes the tool through the MCP protocol.

The client doesn’t need to know how the weather service works internally.

It only needs to understand:

Tool Name
Description
Input Schema

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Building an MCP Client

Now let’s create the other side.

Suppose our AI application needs to consume the weather MCP server.

Add the MCP client starter:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-mcp-client</artifactId>
</dependency>

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Then configure the MCP server connection.

For example, using Streamable HTTP:

spring:
  ai:
    mcp:
      client:
        streamable-http:
          connections:
            weather-server:
              url: http://localhost:8080

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Spring AI can connect to the configured MCP server and discover its tools.

Connecting MCP Tools to ChatClient

Once the MCP client discovers the server’s tools, those tools can be integrated into Spring AI’s tool-calling architecture.

Conceptually:

@Bean
CommandLineRunner demo(
        ChatClient chatClient,
        ToolCallbackProvider mcpTools) {

    return args -> {

        String response = chatClient
                .prompt("What's the weather in Paris?")
                .tools(mcpTools)
                .call()
                .content();

        System.out.println(response);
    };
}

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This is a powerful abstraction.

The application doesn’t need to manually implement every weather function.

The MCP server provides the capability.

The MCP client discovers it.

Spring AI makes the discovered tools available to the model.

The flow becomes:

User
 ↓
ChatClient
 ↓
LLM
 ↓
MCP Tool
 ↓
MCP Client
 ↓
MCP Server
 ↓
Weather API
 ↓
Tool Result
 ↓
LLM
 ↓
Answer

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Spring AI’s current MCP documentation demonstrates this client pattern using ToolCallbackProvider.

MCP Tool Discovery

One of the interesting capabilities of MCP is tool discovery.

Instead of hardcoding:

Tool A
Tool B
Tool C

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the client can connect to an MCP server and discover what capabilities it provides.

For example:

MCP Server
 ↓
tools/list
 ↓
getWeather()
getForecast()
searchAlerts()

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The AI application can then make these tools available to the model.

This creates a more modular architecture.

Multiple MCP Servers

Now imagine our AI assistant needs multiple capabilities.

We could have:

AI Application
      │
      ├── MCP Client
      │
      ├── Weather Server
      │
      ├── CRM Server
      │
      ├── GitHub Server
      │
      └── Internal API Server

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The architecture becomes:

                         AI Agent
                            │
                        MCP Client
                            │
             ┌──────────────┼──────────────┐
             ↓              ↓              ↓
        Weather MCP      CRM MCP       GitHub MCP
             ↓              ↓              ↓
        Weather API       CRM API      GitHub API

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The AI application can consume tools from multiple MCP servers.

This is one reason MCP becomes useful as an AI system grows.

MCP + Spring AI Agents

Now connect this to the previous article.

We previously had:

Agent
 ↓
Tools
 ↓
APIs

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With MCP, we can move the tools outside the application boundary:

Agent
 ↓
MCP Client
 ↓
MCP Servers
 ├── CRM
 ├── Payments
 ├── Search
 └── Internal APIs

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The resulting architecture becomes:

                         User
                           ↓
                       Spring Boot
                           ↓
                        ChatClient
                           ↓
                         Agent
                           ↓
                       MCP Client
                           ↓
             ┌─────────────┼─────────────┐
             ↓             ↓             ↓
          CRM MCP      Payment MCP    Search MCP
             ↓             ↓             ↓
           CRM API     Payment API   Search API

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This creates a modular tool ecosystem.

MCP vs Direct Tool Calling

Let’s compare the two approaches.

Direct Spring AI Tool

ChatClient
    ↓
@Tool
    ↓
Service
    ↓
Database

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Everything lives inside the application.

MCP Tool

ChatClient
    ↓
MCP Client
    ↓
MCP Server
    ↓
Service
    ↓
Database

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The capability provider can be separated from the AI application.

This can be useful when:

• Multiple AI applications need the same capability
• Tools need independent deployment
• Teams own different integrations
• External systems need standardized AI access
• You want a reusable tool ecosystem

MCP Server as a Capability Layer

One useful architectural pattern is:

Business System
       ↓
MCP Server
       ↓
AI Applications

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For example:

CRM
 ↓
CRM MCP Server
 ↓
 ├── Sales Agent
 ├── Support Agent
 └── Internal Assistant

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Instead of implementing CRM integration separately in every AI application, the MCP server becomes the standardized capability layer.

MCP + RAG

MCP doesn’t replace RAG.

They solve different problems.

RAG:

Retrieve relevant knowledge

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MCP:

Connect AI applications to external capabilities

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You can combine them:

                       Agent
                         ↓
             ┌───────────┼───────────┐
             ↓           ↓           ↓
            RAG        MCP Tools    Memory
             ↓           ↓           ↓
        Vector DB    External APIs  Database

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For example:

User:
Can I refund order ORD-10291?

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The agent could:

1. MCP → Get order information
2. RAG → Retrieve refund policy
3. Agent → Compare the two
4. Return answer

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This gives the model both:

Live Data
+
Business Knowledge

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MCP + Memory

Memory can also coexist with MCP.

For example:

User:
Use my preferred delivery address.

Agent:
Which address?

User:
The one I used last time.

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The application may use:

Memory
 ↓
Previous Address

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while MCP provides:

Order Service
 ↓
Update Delivery Address

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The complete flow becomes:

Agent
 ├── Memory
 ├── RAG
 └── MCP
       ├── Orders
       ├── Payments
       └── CRM

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This is becoming a much more complete agent architecture.

MCP Transports

MCP supports multiple ways for clients and servers to communicate.

Common options include:

STDIO
SSE
Streamable HTTP
Stateless Streamable HTTP

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For local process-based integrations:

AI Application
 ↓
STDIO
 ↓
MCP Server Process

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For network-based applications:

AI Application
 ↓
HTTP
 ↓
MCP Server

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In Spring AI 2.x, Streamable HTTP is the current HTTP-oriented approach, while SSE has been deprecated in favor of Streamable HTTP.

STDIO vs HTTP

A simple way to think about it:

STDIO

Application
 ↓
Local MCP Process

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Useful for local integrations and process-based communication.

Streamable HTTP

Application
 ↓
Network
 ↓
MCP Server

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Useful when the MCP server runs as an independent service.

Stateless Streamable HTTP

Client
 ↓
Request
 ↓
Server
 ↓
Response

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This can be useful for stateless, cloud-native service architectures.

The right transport depends on deployment and communication requirements.

MCP Security

This is extremely important.

An MCP server may expose powerful capabilities:

readCustomer()
createInvoice()
refundPayment()
deleteUser()

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Simply exposing those tools does not make them safe.

Spring AI’s MCP server starters do not automatically provide authentication or authorization for network-accessible MCP endpoints. The documentation specifically warns that HTTP-based MCP endpoints need a security boundary before being exposed beyond localhost.

A production architecture should look like:

Client
 ↓
Authentication
 ↓
Authorization
 ↓
MCP Server
 ↓
Tool
 ↓
Business Logic

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Not:

Internet
 ↓
MCP Server
 ↓
Dangerous Tool

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MCP Tool Authorization

Imagine an MCP server exposes:

getCustomer()
updateCustomer()
deleteCustomer()

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Different users should have different capabilities.

For example:

READ
 ↓
getCustomer()

WRITE
 ↓
updateCustomer()

DESTRUCTIVE
 ↓
deleteCustomer()

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Your security layer should determine whether the caller is allowed to invoke each capability.

The model should never be considered the authorization layer.

The application must enforce it.

MCP and Multi-Tenant Systems

MCP becomes particularly interesting in SaaS environments.

Suppose:

Tenant A
 ↓
CRM MCP Server

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and:

Tenant B
 ↓
CRM MCP Server

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The MCP layer must preserve tenant context.

A request might carry:

tenantId
userId
roles
permissions

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The server can then enforce:

Authentication
 ↓
Tenant Resolution
 ↓
Authorization
 ↓
Tool Execution
 ↓
Tenant-Scoped Data

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This is especially important for tools such as:

searchCustomers()
getInvoices()
searchDocuments()
createTicket()

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A model must never be able to use a tool to cross tenant boundaries.

MCP Error Handling

External tools can fail.

For example:

Agent
 ↓
MCP Tool
 ↓
CRM API
 ↓
Timeout

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Your application needs controlled failure behavior.

For example:

Tool Failure
 ↓
Capture Error
 ↓
Return Structured Result
 ↓
Agent
 ↓
Retry / Alternative Tool / Final Response

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The agent might decide:

CRM unavailable.

Try cached customer information.

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Or:

Unable to retrieve the customer's order.
Please try again later.

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The important part is that failures should be observable and controlled.

MCP Observability

When MCP is added to an agent architecture, your observability requirements increase.

You may need to track:

MCP Server
MCP Client
Tool Name
Tool Arguments
Request ID
Latency
Status
Errors
Retries
Model Calls
Token Usage

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A useful trace could look like:

User Request
    ↓
LLM Call
    ↓
MCP Tool Discovery
    ↓
Tool Call
    ↓
CRM API
    ↓
Tool Result
    ↓
LLM Call
    ↓
Final Response

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Without tracing, debugging multi-server agent systems can become difficult.

MCP Doesn’t Replace Your Business Logic

This is another important principle.

Suppose you have:

public RefundResult refundPayment(
        String orderId,
        BigDecimal amount) {
    ...
}

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You shouldn’t move all business logic into an MCP handler.

Instead:

MCP Tool
 ↓
Application Service
 ↓
Business Rules
 ↓
Repository
 ↓
Database

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For example:

@McpTool(description = "Refund an eligible order")
public RefundResult refundOrder(String orderId) {

    return refundService.refund(orderId);
}

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The MCP layer becomes an interface.

Your existing business service remains responsible for the actual business rules.

This keeps the architecture clean.

MCP as an Integration Boundary

One of the strongest ways to think about MCP is as an integration boundary.

Instead of:

AI
 ↓
Everything

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use:

AI
 ↓
MCP
 ↓
Controlled Capabilities

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The MCP layer becomes a contract between AI applications and external systems.

For example:

AI Application
      ↓
MCP
      ↓
CRM

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or:

AI Application
      ↓
MCP
      ↓
Payment System

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or:

AI Application
      ↓
MCP
      ↓
Internal Developer Platform

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A Complete Spring AI + MCP Architecture

Now combine everything from this series:

                              User
                                ↓
                         Spring Boot API
                                ↓
                           ChatClient
                                ↓
                              Agent
                                ↓
             ┌──────────────────┼──────────────────┐
             ↓                  ↓                  ↓
           Memory              RAG              MCP Client
             ↓                  ↓                  ↓
         PostgreSQL          pgvector        ┌─────┼─────┐
                                             ↓     ↓     ↓
                                           CRM  GitHub  Search
                                           MCP    MCP     MCP
                                             ↓     ↓     ↓
                                           APIs  APIs   APIs

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Around the system:

Authentication
Authorization
Tenant Isolation
Observability
Audit Logging
Rate Limiting
Guardrails
Human Approval

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This is a strong foundation for production-oriented AI applications.

When Should You Use MCP?

MCP becomes particularly useful when you have:

Multiple AI applications
        ↓
Shared tools
        ↓
Shared integrations

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For example:

Sales Agent
Support Agent
Developer Agent
Internal Assistant

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all need access to:

CRM
GitHub
Internal APIs
Documentation

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Instead of implementing each integration separately:

Agent A → CRM Integration
Agent B → CRM Integration
Agent C → CRM Integration

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you can create:

CRM MCP Server

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and allow multiple AI applications to consume it.

When You Don’t Need MCP

MCP isn’t automatically required for every AI application.

If your application has:

One Agent
 ↓
One Tool
 ↓
One Internal Service

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direct Spring AI tool calling may be simpler.

For example:

ChatClient
 ↓
@Tool
 ↓
OrderService

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Introducing an MCP server could add unnecessary infrastructure.

A useful rule is:

Use MCP when standardization, reuse, separation, or interoperability provides real value.

Don’t introduce another protocol simply because it is popular.

Direct Tools vs MCP

A simple comparison:

Approach Best suited for Spring AI @Tool Local application capabilities MCP Shared/external capabilities RAG Knowledge retrieval Memory Conversation context Agent Dynamic decision-making

They are not mutually exclusive.

A production system may use all of them:

Agent
 ├── Local Spring AI Tools
 ├── MCP Tools
 ├── RAG
 └── Memory

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The Bigger Picture

Our AI architecture has evolved throughout this series.

We started with:

LLM
 ↓
Response

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Then:

LLM
 ↓
RAG
 ↓
Knowledge

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Then:

LLM
 ↓
Tools
 ↓
Actions

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Then:

LLM
 ↓
Tools
 ↓
Memory
 ↓
Agent

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And now:

Agent
 ↓
MCP
 ↓
External Capabilities

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The architecture is becoming increasingly modular.

A Mental Model for MCP

Remember it this way:

LLM
=
Reason

RAG
=
Retrieve Knowledge

Memory
=
Remember Context

Tool Calling
=
Invoke Capabilities

MCP
=
Standardize Capability Access

Spring Boot
=
Business Application

Agent
=
Coordinate Decisions

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Together:

LLM
+
RAG
+
Memory
+
Tools
+
MCP
+
Business Logic
=
Production AI Application

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Final Takeaways

MCP gives AI applications a standardized way to interact with external tools and resources.

The key ideas are:

• MCP Client connects an AI application to MCP servers.
• MCP Server exposes tools, resources, and prompts.
• Tools allow actions to be performed.
• Resources provide access to information.
• Prompts can provide reusable prompt templates.
• Spring AI supports MCP clients and servers through Boot starters and annotations.
• MCP tools integrate with Spring AI’s existing tool-calling architecture.
• Streamable HTTP is the current HTTP-oriented transport in Spring AI 2.x.
• MCP does not replace your business logic.
• Authentication and authorization must be enforced before exposing network-accessible MCP servers.
• Multi-tenant applications must preserve tenant isolation across MCP calls.
• MCP is particularly useful when capabilities need to be shared across multiple AI applications.
• For simple local integrations, direct Spring AI tools may be sufficient.

The architecture can now look like:

                         User
                           ↓
                        Agent
                           ↓
        ┌──────────────────┼──────────────────┐
        ↓                  ↓                  ↓
      Memory              RAG             MCP Client
        ↓                  ↓                  ↓
    Conversation       Vector DB       MCP Servers
                                             ↓
                              ┌──────────────┼──────────────┐
                              ↓              ↓              ↓
                             CRM          GitHub         Internal APIs

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The important shift is this:

Before:

AI Application
 ↓
Custom Integrations
 ↓
External Systems

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With MCP:

AI Application
 ↓
MCP Client
 ↓
Standardized Protocol
 ↓
MCP Servers
 ↓
External Capabilities

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MCP doesn’t make your AI application automatically intelligent.

It gives your AI application a standardized way to connect to capabilities.

And when you combine MCP with Spring AI’s:

ChatClient
+
Tool Calling
+
RAG
+
Memory
+
Agents

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you get a powerful foundation for building modular AI applications in Java.

What’s Next?

We’ve now connected our AI agent to external capabilities.

But another challenge appears:

One Agent
      ↓
Multiple MCP Servers
      ↓
Multiple Tools
      ↓
Multiple Decisions

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How do we control which tools an agent can access?

How do we handle permissions?

How do we observe agent behavior?

How do we evaluate whether an agent is making the right decisions?

And how do we build reliable AI workflows instead of simply hoping the model does the right thing?

That takes us into the next stage of AI engineering:

Building Production-Ready AI Agents with Spring AI — Guardrails, Evaluation, Observability, and Human-in-the-Loop Workflows.