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MCP Architecture

Core architecture components​

This guide explains the technical architecture of the Model Context Protocol:

  • Key components - Understand the building blocks that make up the MCP architecture
  • Data flow - Learn how information flows through an MCP system
  • Protocol details - Explore the standardized message formats and protocols
  • Deployment options - Discover different ways to deploy MCP in your infrastructure

Overview​

The Model Context Protocol provides a standardized way for applications to interact with AI models while maintaining context and providing additional capabilities. The architecture consists of several key components:

Core Components​

The MCP architecture includes five main components that work together:

  • MCP Server - The central component that processes requests and manages communication
  • MCP Client - The interface applications use to communicate with MCP servers
  • Context Store - The database that maintains conversation history and state
  • Tools - Functions that models can call to perform actions or retrieve information
  • Resources - Data sources that models can access to retrieve relevant information

Core Components​

MCP Server​

The MCP Server is the central component that:

  1. Processes requests from clients
  2. Manages conversation contexts
  3. Handles communication with AI models
  4. Executes tools and queries resources
  5. Returns responses to clients

An MCP Server exposes a standardized API that follows the Model Context Protocol specification.

MCP Client​

The MCP Client provides a convenient way for applications to communicate with MCP Servers. It handles:

  1. Formatting requests according to the MCP specification
  2. Managing authentication
  3. Processing responses
  4. Facilitating streaming

Context Store​

The Context Store is a database or storage system that maintains conversation history and state. It enables:

  1. Persistent conversations across sessions
  2. Efficient context retrieval and updates
  3. Context window management to prevent token limits

Tools​

Tools are functions that models can call to perform actions or retrieve information. They include:

  1. Definition (name, description, parameters)
  2. Implementation (business logic)
  3. Security controls (permissions, rate limiting)

Resources​

Resources provide models with access to external data sources. They include:

  1. Data source connections
  2. Query interpretation
  3. Result formatting
  4. Caching mechanisms

Data Flow​

MCP Communication Flow​

The typical flow of data through the MCP architecture follows these steps:

  1. Client Request - An MCP client sends a message or request to the MCP server
  2. Context Retrieval - The MCP server retrieves the current conversation context
  3. Model API Call - The server sends the request and context to the AI model
  4. Tool Execution - If requested by the model, tools are executed to perform actions
  5. Resource Queries - If requested by the model, resources are queried to retrieve data
  6. Model Completion - The model completes its response with the tools/resources results
  7. Context Update - The server updates the conversation context
  8. Response Delivery - The server sends the response back to the client

MCP Protocol Specification​

The MCP protocol defines the structure of requests and responses between clients, servers, and AI models.

Message Formats​

Here are the standard formats used in MCP communication:

  • Message Format - Structure of user and assistant messages
  • Tool Call Format - Format for requesting tool execution
  • Tool Response Format - Structure of responses from tools

Message Format​

{
  "id": "msg_123abc",
  "type": "message",
  "content": "User's message here",
  "role": "user",
  "timestamp": "2023-09-15T12:34:56.789Z",
  "context_id": "conv_456def"
}

Tool Call Format​

{
  "id": "tool_789ghi",
  "type": "tool_call",
  "tool": "calculator",
  "parameters": {
    "operation": "multiply",
    "a": 5,
    "b": 3
  },
  "context_id": "conv_456def",
  "timestamp": "2023-09-15T12:35:21.123Z"
}

Tool Response Format​

{
  "id": "resp_321cba",
  "type": "tool_response",
  "tool_call_id": "tool_789ghi",
  "result": {
    "result": 15
  },
  "status": "success",
  "timestamp": "2023-09-15T12:35:22.456Z"
}

Deployment Architecture​

Deployment Options​

MCP can be deployed in various configurations to suit different needs:

  • Standalone Deployment - Run as an independent service with its own resources
  • Integration Deployment - Embed within an existing application
  • Serverless Deployment - Deploy as serverless functions that scale automatically

Standalone Deployment​

In this configuration:

  • The MCP server runs as a standalone service
  • It connects to AI model providers via their APIs
  • Clients connect directly to the MCP server

Integration Deployment​

In this configuration:

  • The MCP server is embedded within an existing application
  • It shares resources with the host application
  • Internal modules communicate directly with the MCP server

Serverless Deployment​

In this configuration:

  • The MCP server runs as serverless functions
  • Context is stored in a managed database service
  • Scales automatically with demand

Security Architecture​

Security Layers​

The MCP architecture includes several security layers:

  • Authentication - Verifying the identity of clients
  • Authorization - Controlling access to different capabilities
  • Data Security - Protecting sensitive information
  • Isolation - Ensuring separation between different users and contexts

Authentication​

MCP supports multiple authentication methods:

  • API keys
  • OAuth tokens
  • JWT tokens
  • Custom authentication schemes

Authorization​

Access control is implemented at multiple levels:

  • User-level permissions
  • Role-based access control (RBAC)
  • Tool-specific permissions
  • Resource-specific permissions

Data Security​

Data security measures include:

  • Encryption of sensitive data
  • Context isolation between users
  • Secure credential management
  • Input validation and sanitization

Scalability​

The MCP architecture is designed for scalability:

Horizontal Scaling​

  • Stateless MCP servers can be scaled horizontally
  • Load balancers distribute traffic across instances

Database Scaling​

  • Context stores can be scaled independently
  • Sharding strategies for high-volume deployments

Caching​

  • Response caching for common queries
  • Context caching for active conversations

Next Steps​

Continue Learning​