MCP Architecture: Understanding Model Context Protocol Design

What is MCP Architecture?

MCP architecture defines the structural organization, communication patterns, and component relationships that enable standardized context management across AI systems. The architecture provides a framework for building interoperable AI agents, tools, and services that can efficiently share context while maintaining security, performance, and scalability.

Core Architectural Components

1. MCP Hosts

MCP hosts are applications that initiate and maintain connections to MCP servers, acting as the primary interface for AI systems to access external context and capabilities.

interface MCPHost {
  // Connection management
  connect(serverConfig: ServerConfig): Promise<Connection>;
  disconnect(connectionId: string): Promise<void>;

  // Protocol communication
  sendRequest<T>(request: MCPRequest): Promise<T>;
  handleNotification(handler: NotificationHandler): void;

  // Resource management
  listResources(connectionId: string): Promise<Resource[]>;
  readResource(uri: string): Promise<ResourceContents>;

  // Tool invocation
  listTools(connectionId: string): Promise<Tool[]>;
  callTool(name: string, args: ToolArguments): Promise<ToolResult>;
}

MCP hosts manage the entire lifecycle of connections to MCP servers, handling initialization, authentication, request routing, and error recovery. They provide the interface through which AI models access external context and capabilities.

2. MCP Servers

MCP servers expose context, tools, and capabilities to MCP hosts through a standardized protocol interface. Servers can provide access to databases, APIs, file systems, and other external resources.

interface MCPServer {
  // Server lifecycle
  initialize(params: InitializeParams): Promise<ServerCapabilities>;
  shutdown(): Promise<void>;

  // Resource providers
  registerResourceProvider(provider: ResourceProvider): void;
  handleResourceRequest(uri: string): Promise<ResourceContents>;

  // Tool providers
  registerToolProvider(provider: ToolProvider): void;
  handleToolCall(request: ToolCallRequest): Promise<ToolCallResult>;

  // Prompt management
  registerPromptProvider(provider: PromptProvider): void;
  handlePromptRequest(name: string, args: PromptArgs): Promise<PromptResult>;
}

Servers implement the MCP protocol to expose their capabilities in a standardized way, enabling hosts to discover and utilize these capabilities without requiring custom integration code for each server.

3. Transport Layer

The transport layer handles the low-level communication between MCP hosts and servers, supporting multiple transport mechanisms including stdio, HTTP/SSE, and WebSocket connections.

interface MCPTransport {
  // Connection establishment
  connect(config: TransportConfig): Promise<void>;
  disconnect(): Promise<void>;

  // Message exchange
  send(message: JSONRPCMessage): Promise<void>;
  onMessage(handler: MessageHandler): void;

  // Connection state
  isConnected(): boolean;
  getConnectionInfo(): ConnectionInfo;
}

// Example stdio transport configuration
const stdioTransport: TransportConfig = {
  type: 'stdio',
  command: 'python',
  args: ['-m', 'mcp_server_filesystem'],
  env: {
    FILESYSTEM_ROOT: '/data'
  }
};

// Example HTTP/SSE transport configuration
const httpTransport: TransportConfig = {
  type: 'http',
  url: 'https://api.example.com/mcp',
  headers: {
    'Authorization': 'Bearer token'
  }
};

The transport layer abstracts the communication mechanism, allowing hosts and servers to work with any supported transport without changing their core logic.

4. Protocol Layer

The protocol layer implements the JSON-RPC 2.0-based communication protocol that defines how hosts and servers exchange messages, handle requests, and manage errors.

interface MCPProtocol {
  // Request/Response pattern
  sendRequest(method: string, params: object): Promise<JSONRPCResponse>;
  handleRequest(handler: RequestHandler): void;

  // Notification pattern (no response expected)
  sendNotification(method: string, params: object): void;
  handleNotification(handler: NotificationHandler): void;

  // Error handling
  createError(code: number, message: string, data?: unknown): JSONRPCError;
}

// Example request message
const request: JSONRPCRequest = {
  jsonrpc: '2.0',
  id: 1,
  method: 'tools/call',
  params: {
    name: 'search_database',
    arguments: {
      query: 'SELECT * FROM users WHERE active = true'
    }
  }
};

// Example response message
const response: JSONRPCResponse = {
  jsonrpc: '2.0',
  id: 1,
  result: {
    content: [
      {
        type: 'text',
        text: 'Found 142 active users'
      }
    ]
  }
};

The protocol layer ensures consistent, reliable communication between hosts and servers, with built-in error handling and request tracking.

Communication Patterns

Client-Server Model

MCP follows a client-server architecture where hosts act as clients initiating requests to server endpoints. This unidirectional pattern simplifies connection management and security boundaries.

// Host initiates connection and requests
class MCPClient {
  async connectToServer(serverName: string): Promise<void> {
    const config = await this.loadServerConfig(serverName);
    const transport = this.createTransport(config);
    await transport.connect(config);

    // Initialize the connection
    const capabilities = await this.sendRequest('initialize', {
      protocolVersion: '2024-11-05',
      capabilities: {
        roots: { listChanged: true },
        sampling: {}
      },
      clientInfo: {
        name: 'my-mcp-client',
        version: '1.0.0'
      }
    });

    this.serverCapabilities.set(serverName, capabilities);
  }
}

Request-Response Pattern

Most MCP interactions follow a request-response pattern where the host sends a request and waits for the server’s response.

// Listing available tools
const toolsResponse = await client.sendRequest('tools/list', {});
console.log('Available tools:', toolsResponse.tools);

// Calling a tool
const toolResult = await client.sendRequest('tools/call', {
  name: 'get_weather',
  arguments: {
    location: 'San Francisco',
    units: 'celsius'
  }
});

Notification Pattern

For events that don’t require a response, MCP uses notifications that flow from server to host.

// Server sends notification about resource changes
server.sendNotification('notifications/resources/updated', {
  uri: 'file:///data/config.json'
});

// Host handles the notification
host.onNotification('notifications/resources/updated', (params) => {
  console.log('Resource updated:', params.uri);
  // Refresh the resource if currently in use
  this.refreshResource(params.uri);
});

Resource Management

MCP’s resource system provides standardized access to external data sources including files, databases, APIs, and other content.

interface Resource {
  uri: string;           // Unique identifier (file://, http://, db://, etc.)
  name: string;          // Human-readable name
  description?: string;  // Optional description
  mimeType?: string;     // Content MIME type
}

interface ResourceContents {
  uri: string;
  mimeType?: string;
  text?: string;         // Text content
  blob?: string;         // Base64-encoded binary content
}

// Example: Reading a resource
const resource = await client.sendRequest('resources/read', {
  uri: 'file:///data/customer_records.csv'
});

console.log('Resource content:', resource.contents[0].text);

Resources enable AI models to access external context in a standardized way, supporting context quality assessment and performance monitoring.

Tool System Architecture

Tools in MCP are executable functions that AI models can invoke to perform actions or retrieve information.

interface Tool {
  name: string;
  description: string;
  inputSchema: {
    type: 'object';
    properties: Record<string, JSONSchema>;
    required?: string[];
  };
}

interface ToolCallRequest {
  name: string;
  arguments: Record<string, unknown>;
}

interface ToolCallResult {
  content: Array<{
    type: 'text' | 'image' | 'resource';
    text?: string;
    data?: string;
    mimeType?: string;
  }>;
  isError?: boolean;
}

// Example tool definition
const searchTool: Tool = {
  name: 'search_documents',
  description: 'Search through indexed documents using semantic search',
  inputSchema: {
    type: 'object',
    properties: {
      query: {
        type: 'string',
        description: 'The search query'
      },
      limit: {
        type: 'number',
        description: 'Maximum number of results',
        default: 10
      }
    },
    required: ['query']
  }
};

The tool system enables integration with AI infrastructure while maintaining standardized interfaces for discovery and invocation.

Prompt System Design

MCP’s prompt system provides reusable, parameterized templates that standardize common interaction patterns.

interface Prompt {
  name: string;
  description: string;
  arguments?: Array<{
    name: string;
    description: string;
    required: boolean;
  }>;
}

interface PromptMessage {
  role: 'user' | 'assistant';
  content: {
    type: 'text' | 'image' | 'resource';
    text?: string;
    data?: string;
    mimeType?: string;
  };
}

// Example prompt template
const analysisPrompt: Prompt = {
  name: 'analyze_data',
  description: 'Analyze a dataset and provide insights',
  arguments: [
    {
      name: 'dataset_uri',
      description: 'URI of the dataset to analyze',
      required: true
    },
    {
      name: 'focus_area',
      description: 'Specific area to focus the analysis on',
      required: false
    }
  ]
};

Scalability and Performance Considerations

Connection Pooling

For high-throughput scenarios, MCP implementations should pool connections to frequently used servers:

class MCPConnectionPool {
  private pools: Map<string, Connection[]> = new Map();
  private maxConnectionsPerServer = 10;

  async getConnection(serverName: string): Promise<Connection> {
    const pool = this.pools.get(serverName) || [];

    // Find available connection
    const available = pool.find(conn => !conn.inUse);
    if (available) {
      available.inUse = true;
      return available;
    }

    // Create new connection if under limit
    if (pool.length < this.maxConnectionsPerServer) {
      const conn = await this.createConnection(serverName);
      pool.push(conn);
      this.pools.set(serverName, pool);
      return conn;
    }

    // Wait for connection to become available
    return this.waitForConnection(serverName);
  }
}

Request Batching

Batch multiple requests to reduce round-trip latency:

class BatchedMCPClient {
  private pendingRequests: Map<string, Request[]> = new Map();

  async batchRequest(serverName: string, method: string, params: object): Promise<any> {
    return new Promise((resolve, reject) => {
      const requests = this.pendingRequests.get(serverName) || [];
      requests.push({ method, params, resolve, reject });
      this.pendingRequests.set(serverName, requests);

      // Flush batch after short delay
      if (requests.length === 1) {
        setTimeout(() => this.flushBatch(serverName), 10);
      }
    });
  }

  private async flushBatch(serverName: string): Promise<void> {
    const requests = this.pendingRequests.get(serverName) || [];
    this.pendingRequests.delete(serverName);

    // Send all requests in parallel
    const results = await Promise.allSettled(
      requests.map(req => this.sendRequest(serverName, req.method, req.params))
    );

    // Resolve/reject individual promises
    results.forEach((result, i) => {
      if (result.status === 'fulfilled') {
        requests[i].resolve(result.value);
      } else {
        requests[i].reject(result.reason);
      }
    });
  }
}

Security Architecture

MCP’s security model follows principle of least privilege with clear trust boundaries between hosts and servers.

interface SecurityContext {
  // Authentication
  authenticateServer(serverConfig: ServerConfig): Promise<boolean>;

  // Authorization
  authorizeResourceAccess(uri: string): Promise<boolean>;
  authorizeToolCall(toolName: string, args: object): Promise<boolean>;

  // Sandboxing
  createSandbox(serverName: string): Sandbox;

  // Audit logging
  logAccess(event: AccessEvent): void;
}

For comprehensive security implementation details, see MCP Security and Privacy Considerations.

Best Practices for MCP Architecture

1. Design for Composability

Build MCP servers as focused, single-purpose services that can be composed together:

// Good: Focused server
class FileSystemMCPServer implements MCPServer {
  // Provides file system access only
}

// Good: Composed client
class ComposedMCPClient {
  filesystemServer: MCPServer;
  databaseServer: MCPServer;
  apiServer: MCPServer;
}

2. Implement Robust Error Handling

Handle errors gracefully at every layer:

try {
  const result = await client.callTool('search', { query: 'test' });
} catch (error) {
  if (error.code === -32601) {
    // Method not found
    console.error('Tool not available on this server');
  } else if (error.code === -32603) {
    // Internal error
    console.error('Server error:', error.message);
  } else {
    // Other error
    console.error('Unexpected error:', error);
  }
}

3. Optimize for Latency

Minimize round trips and leverage dynamic context adaptation:

// Bad: Multiple sequential requests
const tools = await client.listTools();
const resources = await client.listResources();
const prompts = await client.listPrompts();

// Good: Parallel requests
const [tools, resources, prompts] = await Promise.all([
  client.listTools(),
  client.listResources(),
  client.listPrompts()
]);

4. Centralize Configuration

Use centralized configuration management to maintain consistency across deployments and enable tool filtering for performance.

Testing MCP Architecture

Comprehensive testing strategies are essential for robust MCP implementations. For detailed testing approaches, see MCP Testing & Quality Assurance.

Comparing Architectural Approaches

Understanding how MCP architecture compares to alternatives like LangChain, RAG, and custom solutions helps inform architectural decisions and integration strategies.

Conclusion

MCP’s architecture provides a robust, scalable foundation for building AI agent infrastructure. By understanding the core components, communication patterns, and design principles, developers can build interoperable AI systems that efficiently manage context while maintaining security and performance. The architecture’s focus on standardization enables rich ecosystems of hosts, servers, and tools that work together seamlessly.

Effective MCP architecture implementation requires careful attention to implementation best practices, performance monitoring, and cost optimization to ensure production-ready deployments.

Related MCP Topics

Deepen your understanding of MCP architecture with these resources:

• MCP Overview - Understand how architecture fits into the complete Model Context Protocol framework
• MCP Context Window Management - Learn how architectural design enables efficient context window optimization
• MCP Token Optimization Strategies - Discover how architecture supports intelligent token management
• MCP Dynamic Context Adaptation - Implement real-time context routing within the MCP architecture
• MCP Implementation Best Practices - Follow proven approaches for deploying MCP architecture
• MCP Security and Privacy Considerations - Understand security boundaries and authentication in MCP architecture
• MCP Integration with AI Infrastructure - Seamlessly integrate MCP architecture with existing AI platforms
• MCP Performance Monitoring - Monitor architectural performance and optimize system behavior
• MCP Cost Optimization Techniques - Leverage architectural patterns for cost-effective operations
• MCP Tool Filtering & Performance - Optimize tool discovery and routing in MCP architecture
• MCP Centralized Configuration - Manage architectural configuration across deployments
• MCP vs Alternatives - Compare MCP architecture with alternative approaches
• MCP Testing & Quality Assurance - Test and validate MCP architectural implementations