Model Context Protocol (MCP) Architecture Explained

MCP architecture addresses one of the biggest limitations of generative AI. While generative AI excels at generating content and answering questions, it struggles to do things. This limitation disconnects it from systems that power real-world business workflows. 

MCP architecture connects large language models (LLMs) with external systems, enabling them to interpret intent, execute tasks, retrieve real-time data, or interface with API tools. Anthropic designed this open standard in November 2024.

MCP architecture acts as a flexible and intelligent integration layer, transforming passive responders into powerful AI agents. Model Context Protocol architecture combines AI’s reasoning capabilities with real-world apps. Whether a user wants to control a device, trigger an alert, or simply query a database.

MCP is not just a protocol but an integration layer evolving with AI advancements that facilitates dynamic reasoning, secure delegation, and real-time system interaction.

In this blog, I will explain the MCP architecture, how it works, and why it is a revolutionary innovation in the AI world. With MCP architecture explained step-by-step, even teams new to AI integration can begin building secure and scalable AI agents.

Understanding MCP Architecture

MCP architecture is built on three core components,- Model, Context, and Protocol. These three components form a standardized framework for AI to connect to external systems.

Model (LLM)

The core component model is the large language model itself, the reasoning engine like ChatGPT. It is the brain of the operations that interprets human intent and performs reasoning, decision-making, and multi-step tasks. However, the model is stateless by design, which means it doesn’t have the native ability to interact with APIs, databases, or Apps. 

Context : What the Model “Sees”

The context is what the model sees at any moment. It can be considered as the eyes and memory of the model. It includes system prompts, user inputs, and system outputs that are dynamically updated during interactions. MCP ensures that the right context is injected at the right time such that the model’s reasoning is personalized, up-to-date, and grounded.

Protocol: How the Model Talks to Tools

The protocol is the core component that defines how the model communicates with the external systems for tasks like requesting data, triggering functions, or returning outputs.  The good thing I like about MCP is that it doesn’t directly encode tool logic inside the model prompt. Instead, it wraps this in a clean, interpretable format like function calls or structured actions for safe and dynamic delegations.

I would say MCP architecture is like a USB-C for AI systems. USB-C has become a universal port that lets us exchange data, video, power, and more through a standard connector. Similarly, the MCP architecture is a universal connector between LLMs and a world of software tools. 

With MCP architecture:

• I can plug AI into a CRM
• I can pull live data from an API
• I can trigger a workflow in a SaaS platform.

Unique Value: Separation of Tool Logic from App Logic

Many teams ask, “How does the MCP work?”. The answer lies in its modular design that separates logic, memory, and execution layers. The separation of tool logic from app logic is one of the powerful features of MCP. 

We don’t have to hardcode workflows or tool usage inside the model prompt or application logic.

• Tool Logic: The tool logic defines what the tool does (APIs, Functions, and Scripts). The tool logic is often built and maintained by the platform teams or system integrators.

• App Logic: This logic defines why and when the tools are used. It defines the flow, user prompts, and business rules and is typically handled by product teams or AI teams.

So, how is this modular approach helpful? MCP standardizes protocol between these two layers allowing them to evolve independently. It means faster innovation, more scalable architectures, and safer deployments of AI in production environments. 

• It helps internal teams improve app UX or prompts without breaking tool integrations.
• Third-party developers can build and publish tools that work across multiple AI applications.
• Security teams can isolate and audit tool access, without slowing down app iteration.

How Does the MCP Work? MCP Architecture Explained

MCP architecture acts as an operational backbone that enables AI models to act on our behalf, reason, and select tools securely in real-time.

a) Step-by-Step Flow: From User Prompt to Actionable Outcome

• User → Host (AI Interface)

A user inputs a prompt like “Generate a report of open tickets and notify the on-call team” or a general query like “ What’s the weather in Switzerland?”. The host application receives this request and initiates a session with the AI model.

• Host → LLM (Compute Engine)

The host sends the prompt and the related memory/context details to the LLM via 

MCP. Then the LLM interprets the intent and determines the high-level tasks 

required.

• LLM → Tool (Via Proxy Layer)

Based on the intent, the LLM uses a structured tool call like an API action template or function calling to interact with the right tools. In this case, the LLM queries the ticketing system and triggers a notification.

• Tool → Result (Execution and Output)

The MCP protocol validates the call, executes the query, and returns the results, including details like success/failure states, data, or alerts.

• LLM → Response (User Output)

The LLM reads the tool output and creates a human-readable reply. 

Eg: “You have 12 unresolved tickets. The on-call team has been alerted.”

b) Tool Discovery: AI That Knows What It Can Do

One of the key differentiators of MCP is Dynamic Tool Discovery. The tool doesn’t need hardcoded knowledge of available tools. MCP’s tool discovery mechanism allows the LLM to query available tools at runtime dynamically. The host lists available tools via a schema like OpenAPI or OpenAI function definitions. 

The LLM queries this list in real time. Then it reviews each tool’s description and parameters and chooses the right one. This way, the AI can adapt to changing plugins, APIs, and services without requiring retraining and manual updates. It’s like giving the AI a menu of available actions and allowing it to reason and choose one. 

For example, when you query for a weather update, and if there are multiple weather APIs available, the LLM can choose one based on factors like response time and accuracy.

c) Real-Time vs Async: Support for Streaming Outputs

MCP architecture is designed to support both synchronous (immediate) and asynchronous (delayed or ongoing) workflows, making it versatile for various use cases.

Real-Time (Streaming)

For use cases that involve responsive interfaces or automation triggers like chatbots, live dashboards, system monitoring, or IoT monitoring, the tool outputs can stream incrementally. As such, the LLM can respond while the action is still happening. 

Eg: stock price updates

Async Support

When it comes to long-running jobs like batch processing or report generation, the MCP proxy can handle task queuing and results are delivered when they become available. Here, the model can respond with status updates or take a “callback when ready” approach.

It means the AI can work both as a chat-style assistant and a background AI agent.

d) MCP Architecture Diagram

MCP Technical Integration

Integrating MCP architecture with an existing stack is simple and easy, especially with microservices and container-based architectures. We can deploy MCP tools independently, and discover and scale them horizontally with ease.

Here’s how we can do it!

a) Adding MCP to a Microservice System

Each tool in the MCP ecosystem operates as a standalone service, typically like a containerized API. These tools communicate over HTTP or gRPC using structured schemas like JSON-RPC or OpenAI’s function/tool calling format.

1. Containerize Each Tool: Package each tool (eg: API Wrapper, Database Query Service as a container (Eg: Docker) that exposes a /manifest endpoint (tool metadata) and an /invoke endpoint (execution logic).
2. Register with a Host or Registry: Either let the host/LLM discover tools on the fly via a discovery API or push the tool metadata to a central registry.
3. Secure Execution via Proxy: Use an MCP proxy layer to validate, rate-limit, and sandbox tool execution to prevent misuse or sensitive data leakage.

For example, a weather API tool runs in a Docker container and listens for MCP requests on /mcp/tool/weather.

Popular LLM models like OpenAI GPT and Hugging Face Models can be easily wrapped as MCP Tools:

• Wrap OpenAI GPT Functions: Create a simple HTTP interface that forwards input/output to the OpenAI API, exposing it as a tool.
• Deploy Hugging Face Transformers: Use FastAPI + Docker to deploy your custom or fine-tuned transformer, adding a manifest file for MCP to recognize its capabilities.

This abstraction allows you to work with a model like a microservice, scaling and upgrading it without changing the app logic. 

b) Fast MCP – Lightweight Python Library for MCP Servers

FastMCP is a lightweight purpose-built Python framework that lets you easily spin up compliant MCP tools. It provides utilities for tool registration, request handling, and context management. 

Example Implementation

from fastmcp import MCPServer, Tool

from transformers import pipeline

Initialize sentiment analysis tool using Hugging Face

sentiment_analyzer = pipeline(“sentiment-analysis”)

Define MCP tool

class SentimentTool(Tool):    def init(self):        super().init(name="sentiment_analysis", description="Analyzes text sentiment")

def execute(self, input_data):

    text = input_data.get("text")

    result = sentiment_analyzer(text)

    return {"sentiment": result[0]["label"], "confidence": result[0]["score"]}

Create MCP server

server = MCPServer(host="0.0.0.0", port=8000)server.register_tool(SentimentTool())

Start server

if name == “main”:    server.run()

It best suits sandboxing, prototyping, and production use, especially in Python-based environments like MLOps, DevOps Automation, and Data Science.

As best practices for integration:

• Use Kubernetes or Docker Compose to orchestrate multiple MCP tools.
• Implement health checks for MCP servers to ensure tool availability.
• Leverage FastMCP’s built-in logging for debugging and monitoring.

c) Managing the Context Lifecycle

1. Context Creation: When a user request arrives, the host app creates a context object containing details like input, system prompts, and metadata.
2. Context Updates: As tools return results, the context is updated to include new data, maintaining a coherent “memory” for the LLM.
3. Context Cleanup: After the response is delivered, the context is archived or purged to prevent memory leaks or data retention issues.

MCP enables multi-step workflows which means we should manage context effectively across:

• Long-running interactions
• Different Stages (User / Model / Tool)
• Potentially sensitive sessions

As a best practice, use a context manager (e.g., Redis or an in-memory store) to handle multi-step interactions, ensuring stateless operation.

Follow these key strategies for context management:

• Scoped Memory: Separate short-term session context from long-term persistent memory and store only what is needed for the task.
• Expire Old Outputs: Set TTLs or timeouts for context entries to avoid memory bloat and hallucination risks.
• Audit Context Content: Sanitize inputs and intermediate outputs so private data between tool invocations is not leaked.
• Tracking Step IDs and Correlation: Tag tool calls with unique interaction IDs so that the full workflows across logs and metrics can be easily traced.

Security & Compliance

One of the major concerns while integrating AI into production environments is security and compliance. Especially in regulated industries, security is of high priority. MCP architecture is designed with security and compliance in mind. 

a) Data Stays Where It Belongs

In a typical MCP setup, the AI model never directly accesses our databases, internal APIs, or files. It only issues structured requests through the MCP proxy which validates, sanitizes, and executes these requests confined to predefined permissions. Context is tightly controlled, which means the LLM can only see and do what it is allowed to. 

b) Must-haves for a Secure MCP Setup

To securely set MCP production-ready, especially in compliance-sensitive contexts, follow these best practices:

• Access Control (RBAC / ABAC): Define which users, roles, and agents can invoke what tools. For instance, in a healthcare setup, define access controls such that only admins can run billing functions and payment processors while the healthcare models can access the patient summaries but not raw EHR logs.
• Environment Isolation and Secure Config: It is recommended to store sensitive keys and credentials in secure .env or vault-managed variables. Make sure that you never embed secrets in the model prompt or tool manifest.
• Logging and Traceability: Log every tool call to get details like who invoked it, when, and with what input and output. Tag tools with Session IDs and user roles for easy forensics. As a best practice, use structured logging (JSON format) to pipe data into SIEM tools like Datadog or Splunk.

c) Auditing AI in HIPAA / PCI Settings

As MCP architecture isolates AI from direct data access, organizations can easily control what is exposed and trace every action. Regarding high-trust environments like Healthcare, Finance, Government, and Telecom, we should ensure security best practices are in place.

• Role-based Tool Access: In High-trust environments, always assign permissions at the tool level. For a healthcare tool, allow clinicians to invoke fetch_patient_summary while restricting researchers to access only de-identified get_stats.
• Token-scoped Permissions: Tokens dictate what tools the LM can access and what parameters it can send. So authenticate each session with a scoped token (JWT, OAuth
• Immutable Audit Trials: Store a tamper-proof log of the original user prompt, AI interpretation (Tool & Parameters), Tool response, and the final model output.
• Prompt and Context Auditing: In sensitive environments, it is important to not just log what the model did but also what it saw to ensure that no PHI/PII was leaked into the AI’s context window.

MCP architecture is not just a protocol. It is a secure, scalable, and flexible architecture that empowers large language models to efficiently integrate with APIs, execute tasks, and securely collaborate with software ecosystems. 

By separating the model logic, tool interfaces, and execution layers, MCP allows developers to build AI workflows without hardcoding tool behavior and scale AI innovation across teams and tech stacks. This advantage is augmented with control access, audit actions, and compliance standards. 

Be it finance, DevOps, customer support, or healthcare, MCP architecture lays a future-proof path to real-world AI. 

Frequently Asked Questions (FAQs)