Article: MCP in the Java World: Bringing Architectural Strategy to LLM Integrations

Introduction: Why MCP Java SDK is important

Large language models are no longer used only for experimental chatbots or individual productivity tools. In enterprises, these models are reshaping how teams interact with systems and make real decisions. They are, for all intents and purposes, real architectural components.

In spite of this fact, current integrations remain fragile. Many teams use vendor-specific mechanisms, embed integration logic into prompts, or expose APIs directly to models. While these approaches are useful during the prototyping phase, they are difficult to scale and offer very limited governance.

This situation is similar to the early days of SOA, when the lack of shared standards resulted in fragile systems that were difficult to develop and integrate. MCP represents the same point of stabilization: a protocol layer that introduces structure, boundaries, and interoperability models within LLM-based architectures.

The Model Context Protocol (MCP) addresses these challenges by introducing a standardized, model-independent way to expose contextual tools and data. MCP defines a protocol-level contract between models and external systems, which avoids hard coding integration semantics into prompts or SDK-specific calls. This shared, standardized contract makes functionality explicit, discoverable, and governable.

The arrival of the Java MCP SDK is particularly relevant in this context. The JVM ecosystem underpins much of the enterprise workload, where architectural discipline is not an aesthetic quirk. Java-based systems must be observable, testable, resilient, and maintainable over the long term. Introducing LLMs into these environments without a comparable level of discipline leads to a misalignment that becomes difficult to manage and resolve.

The SDK-based and MCP-based integrations in Java allow architects to align LLM adoption with existing enterprise architectural practices, applying concepts such as service boundaries, contracts, and control planes to interactions with LLMs.

This article explores MCP and its Java SDK, examining them from this perspective. The goal is not to explain how to write MCP code, but to examine how MCP redefines the standards for LLM integration, what problems it solves, and what trade-offs it introduces in the design of systems that aim to scale beyond the prototyping phase.

MCP 101: A Protocol-Centric View of LLM Integration

To better understand the MCP from an architectural perspective, we need to shift our focus from individual integrations to interaction models. The MCP is neither an agent framework nor a runtime; rather, it is a protocol that defines how models interface with external functionality through a structured contract.

MCP architectures are based on a small set of clearly distinct roles. The host provides the execution environment for the model; the client mediates the model’s requests, while servers expose tools and resources as explicitly defined functionalities. This indirection model is intentional: Models never directly call APIs or infrastructure; they only call what is declared via the protocol. The result is a governance boundary that controls access without interfering with the model’s reasoning.

Figure 1. MCP layered architecture diagram. Source

One of the main features of MCP is its ability to discover features. Instead of preconfiguring the tools that a model can use, MCP allows clients to query servers at runtime to discover features and their associated schemas. This ability makes the system more adaptable and reduces the need for hardcoded features in prompts or agent logic. From an architectural perspective, this mechanism allows loose coupling between models and systems.

MCP also distinguishes between tools and resources. Tools represent the actions the model can request, while resources expose structured contextual data. This distinction allows for a clear distinction between the modes of data access. Read-only contextual data can be managed very differently from state-changing operations, both in terms of security and governance.

Figure 2. MCP Server Composition

As a protocol, MCP also redefines prompt engineering. The context cannot be a static string assembled at runtime; it must instead be a curated, structured set of information provided through explicit interfaces.

Ultimately, MCP represents a shift from model-centric integration to protocol-based design. LLM interactions are treated as distributed system interactions, subject to the same architectural discipline required for any other type of integration.

Inside the MCP Java SDK: Design and Architectural Choices

The Java MCP SDK transforms the abstract principles of Anthropic’s MCP protocol into a concrete implementation for JVM-based systems. Anthropic’s design balances protocol fidelity with Java enterprise best practices. While the protocol focuses on defining abstract roles, message formats, and capability negotiation, the SDK implements these concepts through Java-centric design choices. These design choices include strongly typed models, explicit interfaces for clients and servers, and builders structured for schema-based interactions.

At a high level, the SDK is structured with a multitiered architecture. There is a clear separation between transport mechanisms (such as STDIO for local integrations or HTTP for distributed communication), protocol semantics, and session management. This separation allows teams to adapt the MCP protocol to different interaction models without affecting the underlying logic.

The SDK supports both synchronous and asynchronous interaction models. Internally, it favors non-blocking communication and reactive flows, a model that is well-suited to MCP I/O interactions. At the same time, the SKD exposes higher-level abstractions that can be used in traditional imperative code. Having these two options available allows fully reactive stacks to coexist with legacy or mixed paradigms within enterprise ecosystems.

Integration with frameworks is another key aspect of the SDK. In Java enterprise contexts, the first question is: Does it integrate with Spring? Well, in Spring-based applications, MCP servers can be integrated alongside existing services, benefiting from Spring's distinctive features, such as dependency injection, configuration management, and the management practices already in place for other applications. This design allows MCP to be adopted incrementally, introducing MCP endpoints step-by-step without having to rewrite the application from scratch or reorganize the entire architecture.

It takes very little code to integrate an MCP tool provider into the Spring Framework:

@Configuration
public class McpServerConfiguration {
   @Bean
   ToolCallbackProvider tools(MonitoringTools monitoringTools) {
       return MethodToolCallbackProvider.builder()
               .toolObjects(monitoringTools)
               .build();
   }
}

In this example, MonitoringTools is a standard Spring-managed bean. The methods exposed by the bean become MCP tools simply by registering them with the MethodToolCallbackProvider. Through this mechanism, MCP functionality aligns with the behavior of other services and becomes part of the same dependency graph. Spring handles the lifecycle, configuration, and injection exactly as it does for all other beans in the application.

The Java SDK's greatest strength lies in making protocol concepts explicit. The definitions of the tools require well-defined inputs and outputs, while the resources must be appropriately described and structured. This need for explicitness forces teams to treat MCP servers as delimited contexts rather than as generic integration levels.

From an architectural perspective, these characteristics align MCP servers with well-established patterns, such as anti-corruption layers and API gateways. The SDK provides the building blocks, and the architectural discipline imposed by these boundaries provides long-term value.

Furthermore, by treating the exposure of functionality as an activity to be developed and designed, the SDK prevents the uncontrolled growth of functionality, which is a very common pitfall of LLM integrations. Therefore, entire APIs are not exposed; rather, carefully designed functionality is provided in line with functional and business needs.

In this sense, the Java MCP SDK represents much more than a protocol. It forces development teams to think about LLM integration, treating them as primary elements within the system architecture rather than just external tools to be hooked up.

Designing MCP Servers in Java: Exposing Capabilities, Not APIs

One of the fundamental decisions when using MCP is how to integrate MCP servers into the overall architecture. At first glance, treating an MCP server as a simple adapter that forwards model requests to existing APIs seems straightforward and easy to implement. However, this approach would compromise many of the protocol's advantages.

In enterprise systems, APIs are often designed for use by an LLM. In the best case, they expose low-level, primitive operations and assume specific invocation sequences. In other cases, they reflect historical constraints or organizational issues (refer to Conway's law). Exposing such APIs directly to a model, even through MCP, risks creating the same tight coupling that MCP was intended to solve.

A more effective approach is to treat MCP servers as feature providers. With this approach, MCP servers will then define higher-level tools that encapsulate the domain-specific business features, rather than exposing raw endpoints. Let's look at a ticketing system as an example. Instead of exposing the complete API, an MCP server can be designed to expose tools such as retrieveIncidentSummary, searchRelatedIncidents, and proposeMitigationSteps. These tools show what the model is authorized to do, not what the underlying system is technically capable of performing.

This design creates a very strong parallel between MCP servers and anti-corruption layers. The server is responsible for translating requests from the model into secure, validated interactions with core systems. Before any call reaches the core systems, the input is validated, authorization checks are applied, and the data is modeled correctly. The model should never see internal identifiers, implementation details, overly descriptive error messages, or operations that are subject to side effects, unless these attributes are explicitly allowed.

It is therefore possible to implement MCP servers in Java, either as independent services or by integrating them into existing Spring applications. This implementation approach allows domain services, repositories, and security components to be reused while providing a limited, intentional interface to the model.

As mentioned above, another important distinction to consider is between read-only and state-modifying functionality. Many use cases are developed based on the fact that models can explore data and suggest actions without executing them directly. MCP makes this distinction explicit by encouraging the development of granular, detailed tools. For example, an MCP server can expose a tool that evaluates an action’s impact or prepares a request, leaving the execution of the action to a separate human-driven process.

All of this capability cannot be managed without good operational practices. MCP servers provide a natural point for observability and auditing. Each invocation of the tool can be logged, tracked, and analyzed independently of the model's internal reasoning.

MCP Clients and Orchestration: Where Models Meet Architecture

MCP servers decide which features are available. MCP clients choose how these features are used. In early LLM integrations, orchestration logic was directly embedded in prompts or agent frameworks, complicating maintainability and understanding of runtime relationships.

An MCP client acts as an intermediary between the model and one or more MCP servers. It is responsible for identifying available tools, managing sessions, and coordinating calls to tools. It is a key component within the architecture, not a passive communication layer.

In a Java ecosystem, we can integrate MCP clients into services that orchestrate complex workflows, such as an operational assistant that uses an MCP client to query multiple servers (monitoring, ticketing, and documentation), and combine their responses in a context suitable for the model. This orchestration logic now resides in the code rather than the prompt, making versioning and testing easier.

One of the key advantages of this model is explicit control of the interaction flow. Call sequences, timeouts, partial error handling, and fallbacks. The client can handle all these situations, deciding independently when to interrupt the model's operations and pass the ball to a human operator. In a prompt, implementing these features is really complicated, but in application code it is much easier.

MCP clients play a central role in context management. Instead of manually assembling large prompts, clients can accurately decide which resources are exposed to the model at any given moment. This choice minimizes context and greatly reduces the risk of sensitive or irrelevant information leaking.

Architecturally speaking, MCP clients are nothing more than orchestrators in a distributed system. They manage conversations and treat them as streams of calls to services. This approach breaks down the complexity of LLM integration into three issues: state management, idempotence, and error handling. These are extremely clear and opinionated issues for developers of Java-based distributed systems.

MCP vs. Native Tool Calling: Trade-Offs and Design Considerations

Using MCP comes at a cost. Compared to the native ways of calling tools offered by LLM providers, MCP adds layers and abstractions. For small or experimental projects, this additional cost is likely to outweigh the benefits.

Calling tools natively is easier and more straightforward: The model calls a function, receives a response, and moves on. This simplicity has a side effect: strong coupling. Tool definitions are often embedded in prompts or SDK configurations, making versioning and independent model management difficult.

MCP, on the other hand, standardizes in a protocol, externalizing definitions and interactions with tools. The standardization adds complexity, but also architectural clarity. Tools become first-class entities with explicit schemas, properties, and lifecycles. They can be managed, controlled, and developed without modifying or reconfiguring the model.

Another trade-off concerns observability. Calling native tools often means blurring the line between model reasoning and tool interaction: How do I track failures and unexpected behaviors? MCP's client-server model establishes clear boundaries and responsibilities, allowing metrics to be recorded and tracked.

In terms of performance, MCP introduces network hops and protocol overhead that must be evaluated in latency-sensitive scenarios. It is a trade-off between predictability, observability, and testability on the one hand, and pure performance on the other. It depends on the requirements that are more important for your solution.

The real advantage of MCP lies in its architectural approach. It allows us to align LLM-based systems with other distributed systems, treating them all consistently with the same established operational, development, and testing practices. If long-term maintenance, security and the correct division of responsibilities (including organizational ones) are more important in our context than rapid experimentation, then MCP is the pattern to apply.

Security, Governance, and Observability: Making MCP Operable at Scale

Once the experimental phase is over, security and governance immediately become the main concerns. MCP does not eliminate these problems, but rather offers architectural leverage points that cannot normally be applied in prompt-based or native model integrations.

By limiting access at the protocol level, MCP inherently applies the principle of least privilege. This is very different from approaches in which models interact directly with the entire API set or a database, often using generic credentials and poorly defined permissions.

This model can be easily integrated with existing security frameworks: Authentication and authorization controls can be enforced at the MCP server boundary, imposing mechanisms such as OAuth, mTLS, or enterprise identity providers. Security is not delegated to the model. An LLM does not decide whether an operation is allowed, but can only request what the MCP server explicitly exposes.

Governance can also be applied to the lifecycle of tools. Over time, new use cases drive the creation of new tools, so it is essential to apply governance policies similar to those used in API management: versioning, deprecation policies, documentation, and property exposure.

Finally, context management also has implications for security and compliance. Context is treated as a managed resource rather than an unstructured prompt, allowing MCP to apply data minimization strategies. Over time, sensitive information can be exposed, censored, or selectively restricted, depending on the use case. Within regulated ecosystems, this control helps reduce the risk of unintended data leaks and simplifies compliance with data protection regulations.

Case Study: An Enterprise Operations Assistant Built on MCP

Let's try to better understand how these concepts work, using the design of an enterprise operations assistant as an example. The goal of this system is to help service managers and application maintenance teams respond to incidents by correlating signals and suggesting solutions, without giving the LLM direct control over production systems.

Figure 3. Enterprise Operations Assistant

The Problem Space

In the vast majority of enterprises, operational knowledge for supporting production is scattered across monitoring platforms, ticketing systems, runbooks, and internal documentation. When an incident occurs, support teams must manually piece together metrics, logs, and historical data. Time is of the essence. LLMs are ideal tools for synthesizing and aggregating information and providing insights, but integrating them into operational workflows in this way can be extremely risky.

The direct approach involves exposing monitoring APIs and ticketing systems directly to the model, leveraging calls to native tools. This solution creates a coupled, fragile, and potentially dangerous configuration: The model has access to all data, very limited data validation, and, above all, very little oversight. In situations like these, it is difficult to control actions after the fact and apply consistent security constraints.

MCP-Based Architecture

Within an MCP-based design, we introduce clear architectural boundaries; for each operational domain, we expose dedicated MCP servers:

• An MCP monitoring server provides a read-only tool, such as getSystemMetrics.
• An MCP knowledge server provides runbooks and historical incident reports as structured resources with a tool to getRecentIncidents.
• An MCP ticketing server allows incident report drafts to be created, but does not allow tickets to be sent and opened without human approval.

Each MCP server has its own operating logic and validates access to the domain's specific features. The tools are designed based on the operating environment rather than raw API functionality.

An MCP client integrated into the Operations Assistant coordinates interactions among these servers, managing context assembly and deciding which resources should be exposed based on the incident being analyzed. The model receives a selected view of the operational landscape, so that &nbit can reason effectively without requiring excessive privileges or information.

Interaction Flow

When the operator asks the MCP client something like, "Why has service X been experiencing problems in recent days?", behind the scenes, the client:

• Asks the MCP monitoring server for recent metrics of the system.
• Retrieves relevant runbooks and past incidents from the MCP knowledge server.
• Creates a targeted context and presents it to the model.
• Allows the model to propose hypotheses and recommendations for their resolution.
• Creates, if desired, a draft incident report for review and evaluation.

The model never directly interacts with production systems and provides ideas and recommendations for resolving issues, leaving the burden of resolution to the operator.

Hands-On Code

Let's analyze what has been built at the code level. To implement the MCP client, the spring-ai-starter-mcp-client was used, combined with OpenAI with spring-ai-starter-model-openai to implement the ChatClient part.

The client exposes an API that invokes the analyze() method. This method, in turn, invokes the getSystemMetrics tool to retrieve system metrics, the getRecentIncidents tool to retrieve recent incidents, and then builds the context to feed to ChatGPT to retrieve relevant information.

public String analyze() {
   try {
       McpSchema.CallToolRequest systemMetricsRequest = new McpSchema.CallToolRequest("getSystemMetrics", Map.of());
       McpSchema.CallToolResult systemMetricsToolResult = mcpClient.callTool(systemMetricsRequest);
       SystemMetrics systemMetricsTool = parseSystemMetrics(systemMetricsToolResult);
       McpSchema.CallToolRequest recentIncidentsRequest = new McpSchema.CallToolRequest("getRecentIncidents", Map.of());
       McpSchema.CallToolResult recentIncidentsToolResult = mcpClient.callTool(recentIncidentsRequest);
       List<Incident> recentIncidents = parseRecentIncidents(recentIncidentsToolResult);


       StringBuilder incidentsSection = buildIncidentsSection(recentIncidents);
       // Analysis with OpenAI via Spring AI ChatClient
       return chatClient
               .prompt()
               .user(buildUserPrompt(systemMetricsTool, incidentsSection))
               .call()
               .content();
   } catch (Exception e) {
       return "Error during the analysis: " + e.getMessage();
   }
}

The prompt is constructed as follows:

private static String buildUserPrompt(SystemMetrics systemMetricsTool, StringBuilder incidentsSection) {
   return String.format(
           """
                   Analyse the following system metrics and recent incidents and provide operational recommendations:
                  
                   - Average latency: %d ms
                   - Error rate: %.2f%%
                   - Timestamp: %s
                  
                     Recent Incidents:
                     %s
                  
                   Constraints:
                   1) Highlight possible causes (DB, network, CPU/memory saturation, external dependencies).
                   2) Suggest actions in order of priority (quick wins -> structural interventions).
                   3) Indicate any additional metrics to be collected.
                   """,
           systemMetricsTool.avgLatencyMs(),
           systemMetricsTool.errorRate() * 100,
           systemMetricsTool.timestamp(),
           incidentsSection
   );
}

On the MCP Server side, we simply exposed the two tools mentioned above, using spring-ai-starter-mcp-server and spring-ai-starter-mcp-server-webmvc. For simplicity, the tool responses are mocked; in real use cases, the MCP server will integrate with the APIs for the monitoring tool, the knowledge tool, and eventually the ticketing tool.

@Tool(description = "Retrieve current system metrics including response time, error rate and timestamp")
public SystemMetrics getSystemMetrics() {
   return new SystemMetrics(120,0.02, Instant.now());
}


@Tool(description = "Retrieve list of recent incidents with their ID, description, timestamp and status")
public List<Incident> getRecentIncidents() {
   return List.of(
           new Incident("INC-42","Database latency spike", Instant.now().minusSeconds(3600), "Resolved"),
           new Incident("INC-43","Cache eviction anomaly", Instant.now().minusSeconds(7200), "Investigating")
   );
}

By calling the client-side API, we get the following result (the result is truncated for ease of reading):

Possible Causes: 1. Database Latency Spike: This could be due to a sudden increase in database read/write operations, or a poorly optimized query…….
Actions in Order of Priority: 1. Investigate the Cache Eviction Anomaly: Since the status of this incident is still under investigation, this should be the top priority…
Additional Metrics to be Collected: 1. Database operation times: This would help identify any problematic queries or operations…..

This content can be used to open a ticket with a preliminary analysis of the current situation, possible causes, and solutions.

Architectural Lessons

This case study highlights some fundamental aspects of the MCP architecture. First of all, it shows how MCP servers function as an anti-corruption layer, protecting core systems and providing predefined, controlled interaction in all cases. Second, it shows how MCP clients enable explicit orchestration and context management, shifting complexity from prompt construction to source code. Finally, it illustrates how governance and observability are integrated requirements that are naturally present in the architecture.

The result is a system that leverages LLM strengths (e.g., reasoning, synthesis, and explanation) while maintaining the security and reliability required in a business environment, such as operations.

Lessons Learned: When MCP Java SDK Makes Sense and When It Does Not

As with any architectural choice, value depends primarily on context. MCP is not a universal solution for all LLM integrations. It is not intended to replace simpler, more straightforward approaches when they are sufficient, especially during prototyping. To make informed decisions, it is important to understand when the MCP Java SDK adds value and when we are just introducing unnecessary baggage to govern and maintain.

One of the most important lessons is that MCP delivers its greatest value in environments where interactions with LLMs must coexist with business constraints. A call to ad hoc tools is unlikely to produce strong governance, with clear boundaries and maintainability, like MCP can. Explicit contracts, feature discovery, separation of responsibilities, and minimal exposed surface area are concepts that help MCP align with design practices in enterprise contexts.

Another key point is that MCP is most effective when it is used to expose intent rather than infrastructure. These benefits cannot be achieved by treating MCP servers as simple wrappers around existing APIs. You have to invest in designing meaningful, business-aligned capabilities, resulting in clearer boundaries, safer interactions, and much more predictable and observable behavior. This is a lesson already learned from years of experience in API design: The quality of abstraction is more important than the choice of protocol.

MCP also encourages and enforces a healthier, more consistent distribution of responsibilities, assigning everything to source code rather than to prompts. Authorization, validation, and orchestration are handled by systems designed for those tasks, leaving the LLM free to focus on reasoning and synthesis: less cognitive load and better system resilience.

However, all this capability comes at a price. MCP introduces additional components, communication overhead, operational complexity, and more points of failure. For small teams or short-lived experiments, these costs likely outweigh the benefits. In these cases, native tools or direct integrations may be perfectly adequate solutions, provided you understand their limitations.

One such limitation lies in the organization's readiness for this new paradigm. MCP assumes a certain level of architectural maturity, where teams must be aware of the need to define contracts, manage versions, and operate on shared infrastructure. Ignoring these responsibilities can make MCP just another layer of integration rather than a discipline.

Ultimately, MCP must be considered a long-term architectural investment. Its return grows with the development of systems and the scalability of the team and solution. When these conditions are present, MCP provides a foundation that can support sustainable growth rather than a short-term coexistence of convenience.

Conclusions: MCP as a Control Plane for LLM-Aware Systems

The integration of LLMs into enterprise systems is marking a radical change in the way software architectures are conceived and designed. LLMs introduce probabilistic reasoning into an environment traditionally governed by deterministic logic. Managing this change requires a well-structured architectural discipline; intelligent prompts or native extensions from individual suppliers are not enough.

MCP represents a step in this direction. MCP defines a standardized way to expose tools and context, redefining LLM integration as a protocol challenge rather than an implementation detail. This change allows the application of principles much more familiar to architects, such as separation of responsibilities, least privilege, observability, and the existence of explicit and documented contracts, to a whole new class of systems.

The availability of the MCP Java SDK marks a fundamentally important step for the entire Java ecosystem and the enterprise world. It allows organizations to integrate LLMs without sacrificing the architectural rigor that underpins their most critical systems. Instead of treating LLMs as exogenous and exceptional components that operate outside the law and established boundaries, MCP allows them to be included as regular and prominent participants within the enterprise architecture.

However, MCP is not about providing new capabilities. Rather, it is about giving existing capabilities a more essential and responsible form. It provides a control plane for observing, governing, and developing interactions between models and systems over time. This governance becomes essential as LLM-based capabilities move closer to core business processes.

To be clear, MCP does not eliminate the inherent uncertainty of LLMs, nor does it guarantee greater correctness. What it offers is a way to manage uncertainty within well-defined and pre-established architectural boundaries. For organizations seeking to move beyond experimentation and toward sustainable AI adoption, this distinction is critical.

As LLMs continue to develop, protocols such as MCP will play the same role as HTTP and the REST paradigm did in previous architectural transitions. The goal is not to dictate implementation details, but to establish shared expectations and thereby produce sustainable interoperability between systems.

This work is just the beginning: LLMs continue to reshape system design and MCP is the protocol through which Java-based enterprise architectures will adapt to integrate artificial intelligence into their own ecosystems. The code in this article is available at the following links (mcp-client and mcp-server).