The Model Context Protocol (MCP) architecture simplifies AI integration complexity using clear architectural patterns, including Facade/API Gateway, Adapter, Sidecar, and Orchestrator. By isolating services, standardizing interfaces, and reducing orchestration overhead, MCP makes complex AI systems more maintainable, scalable, and robust. This is ideal for building reliable, production-ready agentic AI applications.
Introduction
When you build systems powered by large language models (LLMs), whether fully agentic or interactive human-driven applications, you may quickly notice that the architectural complexity of the system increases significantly. Your AI needs to interact seamlessly with a multitude of external APIs, tools, and data sources. Without care, your system may become a tangled mess of plugins, bespoke integrations, and brittle code.
Anthropic’s Model Context Protocol (MCP), launched in Nov’24, has quickly become a game-changer for architects and developers designing complex AI-driven systems. Rather than just another integration technology, MCP’s internal architecture directly addresses and elegantly solves the fundamental integration and complexity challenges that developers are beginning to encounter as they develop increasingly complex AI and agentic systems.
In this article, I’ll clearly unpack MCP’s architecture and illustrate why its thoughtful design has a profoundly meaningful impact on simplifying our architectural patterns and workflows. I will highlight four specific ways in which MCP can simplify your system’s architecture:
- Unified and Simplified Access (Facade/API Gateway)
- Universal Compatibility (Adapter)
- Modular Decomposition and Isolation (Sidecar)
- Intelligent Coordination (Simplifying the Orchestrator)
How MCP Actually Works: Step-by-step
A typical system integrating MCP involves three clearly defined architectural components:
Host Application
Your host application is where your AI lives. It could be a desktop app like Anthropic’s Claude Desktop, or perhaps an AI-enhanced IDE like Cursor. The host manages interactions between the user and your AI model, presenting outputs and gathering user input.
MCP Client (Embedded inside your Host Application)
Inside your host app, you run an MCP Client. This is essentially a lightweight embedded connector that manages standardized communication with external MCP Servers. Think of the MCP Client as your single unified gateway. Rather than making direct, specialized API calls to each external service, your host makes structured MCP requests via JSON-RPC to the client. The MCP Client then handles the complexities of communicating with MCP servers.
MCP Server (External service providers)
MCP Servers run independently outside your host. Each MCP server provides specialized external capabilities like fetching data (resources), performing actions (functions), or offering structured prompts (callback instructions). Filesystem MCP Server (allowing file operations on your system), Slack MCP Server (for messaging), and GitHub MCP Server (for repository interactions) are a few popular examples. MCP Servers organize their capabilities into three structured primitives:
- Resources: Accessing read-only data (like files, Slack messages, or emails).
- Tools: Actions your AI can perform (sending messages on Slack, creating GitHub commits, or modifying files).
- Prompts: Providing structured instructions or reusable templates to assist the AI.
End-to-End MCP Interaction Example: Filesystem Integration
Putting it all together, the following sequence diagram illustrates how MCP enables a structured interaction between the user, the host application, MCP client, and the filesystem MCP server to discover, retrieve, and summarize content from external resources.
Why MCP’s Architectural Simplicity Matter
With the core MCP structure now clearly defined, let’s dive deeper into four key architectural strengths enabled by MCP’s design, demonstrating how these design choices can help simplify your system’s architecture significantly.
Unified and Simplified Access: MCP as a Facade/API Gateway
In a typical Agentic-AI system, your host application must interact with various external services, each of which may have unique protocols, authentication methods, and operational details. Handling each of these directly within your host application will introduce complexity and tight coupling, significantly increasing development and maintenance overhead.
MCP simplifies this dramatically by providing your host application with a unified, consistent way to interact with external services. Specifically, the MCP client embedded in your host acts as a single entry point for all interactions. It encapsulates the underlying complexity by presenting your host application with a standardized, uniform interface using JSON-RPC calls such as tools/call or resources/list. Your host never directly touches external APIs, connection pooling, error handling, or other backend-specific issues. It simply issues a structured MCP request, and the complexity behind it is transparently handled by MCP.
For example, when your AI logic in the host application needs data from your local filesystem, it sends a standardized MCP request (list_directory) through the MCP client. The MCP client forwards this request to the Filesystem MCP Server, which in turn manages low-level filesystem operations and responds in a structured, predictable format. Similarly, when your host needs data from a database, it issues a query_database MCP request, and the Database MCP Server handles the SQL queries and returns structured data.
Architecturally, this approach precisely aligns with the Facade or API Gateway pattern, widely adopted in cloud-native systems. Just as a cloud API gateway hides multiple backend services behind a single unified endpoint, MCP’s embedded client similarly hides all external complexities behind a single unified protocol and interaction method, making your host application simpler, modular, and far easier to maintain.
Universal Compatibility: MCP as an Adapter
In addition to providing unified access, MCP addresses another critical challenge: managing diverse and incompatible external APIs. Typically, integrating different external services directly into your host application requires writing and maintaining multiple custom adapters, each tailored specifically to a service’s unique interface.
MCP solves this elegantly through dedicated MCP servers, each acting as an individual adapter. Every MCP server explicitly translates the complexity of a specific external service into MCP’s standardized JSON-RPC interface. Each server is essentially responsible for converting the specific protocols, authentication methods, and data formats of its external service into predictable, uniform MCP responses and requests.
For instance, Anthropic’s Filesystem MCP server adapts OS-specific file operations into simple MCP methods such as read_file and list_directory. It handles filesystem permissions, path management, file I/O errors, and related complexities internally. Similarly, a Database MCP server acts as an adapter between your standardized MCP requests (query_database) and the actual database management system, handling SQL query generation, database connections, result parsing, and formatting.
In architectural terms, this is precisely the Adapter pattern. Each MCP server provides a clearly defined standardized interface while internally translating it into the specific protocols required by the external backend system. As a result, your host application never needs to implement service-specific integration logic; you simply plug in new MCP servers to instantly gain new capabilities. This adapter-based architecture significantly simplifies system design, accelerates integration, and provides unmatched flexibility when integrating or replacing external services.
Modular Decomposition and Isolation: MCP as Sidecars
When integrating multiple external services into your application, ensuring reliability and maintaining clear boundaries between components becomes critically important. Traditionally, applications handle external integrations directly, often by embedding complex integration logic within your application’s own process. For instance, directly calling external APIs or including third-party libraries can introduce stability risks. If an external call fails, hangs, or leaks memory, it can degrade or destabilize your entire host application.
MCP explicitly solves this problem through an architectural pattern known as the sidecar pattern. In MCP architecture, as we discussed before, each external integration is encapsulated as a dedicated MCP server. They always run as a completely isolated and independent process, never within your host application’s own memory or runtime environment. The MCP client inside your host application communicates exclusively with these external MCP servers using a standardized JSON-RPC protocol. This strict isolation ensures that your host application and external integrations remain clearly separated and independent.
For example, consider the Filesystem MCP server. Rather than embedding filesystem operations directly in your host application’s logic, the Filesystem MCP server runs independently, either as a separate local process or a remotely managed microservice. If the Filesystem MCP server encounters errors or becomes unavailable, your host application remains stable and unaffected. You can safely restart or scale the Filesystem MCP server without impacting your application’s uptime or performance.
By clearly isolating external interactions as separate, independently managed components, MCP keeps your core application clean, stable, and resilient to individual integration failures.
Intelligent Coordination: MCP simplifying your Orchestrator System
In complex agentic systems, orchestrating multiple external service interactions quickly becomes challenging. As your AI iteratively thinks, plans, and executes—retrieving information, deciding on next steps, and maintaining memory and context—complexity can rapidly escalate. If your application directly integrates multiple external APIs within custom orchestration logic, complexity and fragility can escalate rapidly. Even small changes or new integrations can compound, making the orchestration logic brittle, cumbersome, and increasingly difficult to maintain.
For example, consider an agentic scenario where your AI generates a comprehensive market report. It first gathers current market news via a Web Scraping MCP Server, then queries structured market data from a Database MCP Server. Next, it retrieves relevant historical context from local files through a Filesystem MCP Server. After each retrieval step, the orchestrator feeds results back into the LLM to iteratively reason, maintain memory, and decide subsequent actions. Without careful orchestration, complexity in managing state, retries, errors, and iterative reasoning loops can exponentially increase, making the system unstable.
MCP significantly simplifies orchestration complexity. While MCP itself isn’t explicitly an orchestrator, it provides a standardized JSON-RPC interaction interface (tools/call, resources/list). This consistent, structured communication ensures that your orchestrator logic remains clean, predictable, and manageable. With MCP handling all external complexities behind a standard protocol, your orchestrator avoids exponential growth in complexity and remains robust, scalable, and maintainable, even in highly iterative agentic workflows.
Closing Thoughts
If you’re building complex AI systems, agentic or otherwise, you should strongly consider integrating with MCP, given the clear architectural benefits we’ve explored:
- Reduced Integration Complexity (N+M vs. N×M): Rather than dealing with a complicated web of custom integrations between your application and external services (the traditional N×M scenario), MCP provides universal compatibility and unified access, simplifying your integrations to a straightforward N+M model.
- Improved Stability and Isolation: Each MCP server acts as an isolated modular sidecar, encapsulating external interactions independently. A failure in one integration won’t cascade into others or compromise your main application, ensuring resilience and easier system maintenance.
- Scalable and Independent Growth: With MCP, you can scale individual services, such as your filesystem, database, or web-scraping integrations, independently of each other. This flexibility means you don’t need a full system redeployment for incremental improvements or performance enhancements.
- Simplified Agent Workflow Orchestration: MCP’s standardized request-response interactions allow your orchestrator logic to remain clean, manageable, and robust. This dramatically simplifies memory management, state handling, error processing, retries, and complex iterative workflows, enabling your orchestration logic to scale efficiently.
By adopting MCP, you gain architectural simplicity, flexibility, and reliability. These are key attributes that can significantly enhance your AI application’s overall quality and maintainability.
Let me know what you think and if there are any other advantages (or pitfalls) of MCP you’ve encountered. Feel free to leave a note below or contact me directly via my website.
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Disclaimer
The views and opinions expressed in my articles are my own and do not represent those of my current or past employers, or any other affiliations.
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