MCP Architecture

Deep dive into the technical architecture of the Model Context Protocol. Learn how clients, servers, and transports work together.

System Architecture

MCP follows a client-server architecture where clients (like MCPCodex) connect to specialized servers that provide specific capabilities. All communication happens over JSON-RPC 2.0 protocol.

MCP ClientMCPCodex
JSON-RPC
MCP ServerFile System
ResourcesFiles, Data

MCP Clients

Applications that need AI capabilities. They connect to MCP servers to access tools and resources.

  • • MCPCodex Platform
  • • Claude Desktop
  • • Custom applications
  • • IDE extensions

MCP Servers

Specialized services that expose specific capabilities to AI models through standardized interfaces.

  • • File system access
  • • Database queries
  • • API integrations
  • • Custom tools

Transports

Communication channels between clients and servers, supporting various connection methods.

  • • Standard I/O (stdio)
  • • HTTP/WebSocket
  • • Custom transports
  • • Secure connections

Protocol Specification

JSON-RPC 2.0 Foundation

MCP is built on JSON-RPC 2.0, providing a standardized way for clients and servers to communicate. Here's the initialization handshake:

protocol-init.json
{
  "jsonrpc": "2.0",
  "method": "initialize",
  "params": {
    "protocolVersion": "2024-11-05",
    "capabilities": {
      "tools": {
        "listChanged": true
      },
      "resources": {
        "subscribe": true,
        "listChanged": true
      },
      "prompts": {
        "listChanged": true
      },
      "logging": {}
    },
    "clientInfo": {
      "name": "MCPCodex",
      "version": "2.0.0"
    }
  },
  "id": 1
}

Core Capabilities

Tools

Functions that AI models can call to perform actions or retrieve information.

  • tools/list - List available tools
  • tools/call - Execute a tool
  • • Dynamic tool discovery

Resources

Data sources that AI models can read from, like files or database entries.

  • resources/list - List resources
  • resources/read - Read resource content
  • • Subscription notifications

Prompts

Reusable prompt templates with parameters for common AI interactions.

  • prompts/list - Available prompts
  • prompts/get - Get prompt template
  • • Parameter substitution

Logging

Structured logging and monitoring for debugging and observability.

  • logging/setLevel - Set log level
  • • Structured log messages
  • • Performance metrics

Server Implementation

MCP servers are the backbone of the protocol. They expose domain-specific functionality through a standardized interface. Here's how to implement a custom MCP server:

mcp-server.js
// MCP Server Implementation
import { Server } from '@modelcontextprotocol/sdk/server/index.js';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';

class CodeAnalysisServer {
  constructor() {
    this.server = new Server(
      {
        name: "code-analysis-server",
        version: "1.0.0",
      },
      {
        capabilities: {
          tools: {},
          resources: {},
          logging: {},
        },
      }
    );

    this.setupHandlers();
  }

  setupHandlers() {
    // Handle tool calls
    this.server.setRequestHandler('tools/call', async (request) => {
      const { name, arguments: args } = request.params;
      
      switch (name) {
        case 'analyze_code':
          return await this.analyzeCode(args.file_path, args.analysis_type);
        case 'suggest_improvements':
          return await this.suggestImprovements(args.code, args.language);
        default:
          throw new Error(`Unknown tool: ${name}`);
      }
    });

    // Handle resource requests
    this.server.setRequestHandler('resources/read', async (request) => {
      const { uri } = request.params;
      return await this.readResource(uri);
    });

    // List available tools
    this.server.setRequestHandler('tools/list', async () => {
      return {
        tools: [
          {
            name: 'analyze_code',
            description: 'Analyze code for issues and improvements',
            inputSchema: {
              type: 'object',
              properties: {
                file_path: { type: 'string' },
                analysis_type: { 
                  type: 'string',
                  enum: ['security', 'performance', 'style', 'bugs']
                }
              },
              required: ['file_path', 'analysis_type']
            }
          }
        ]
      };
    });
  }

  async analyzeCode(filePath, analysisType) {
    // Implementation details...
    return {
      content: [
        {
          type: 'text',
          text: `Analysis results for ${filePath}`
        }
      ]
    };
  }

  async run() {
    const transport = new StdioServerTransport();
    await this.server.connect(transport);
  }
}

const server = new CodeAnalysisServer();
server.run().catch(console.error);

Server Lifecycle

Initialize
Set up server capabilities and handlers
Connect
Establish transport connection
Handle Requests
Process incoming JSON-RPC messages
Cleanup
Close connections and free resources

Security Considerations

  • • Validate all input parameters
  • • Implement proper access controls
  • • Sanitize file paths and URIs
  • • Rate limit tool calls
  • • Log security events
  • • Use secure transport layers

Client Implementation

MCP clients orchestrate multiple servers to provide comprehensive AI capabilities. Here's how MCPCodex implements its client architecture:

mcp-client.js
// MCP Client Implementation
import { Client } from '@modelcontextprotocol/sdk/client/index.js';
import { StdioClientTransport } from '@modelcontextprotocol/sdk/client/stdio.js';

class MCPCodexClient {
  constructor() {
    this.servers = new Map();
  }

  async connectToServer(serverName, command, args = []) {
    try {
      // Create transport for server communication
      const transport = new StdioClientTransport({
        command,
        args
      });

      // Create client instance
      const client = new Client(
        {
          name: "mcpcodex-client",
          version: "2.0.0"
        },
        {
          capabilities: {
            roots: {
              listChanged: true
            },
            sampling: {}
          }
        }
      );

      // Connect to server
      await client.connect(transport);

      // Initialize the connection
      await client.initialize();

      // Store the client
      this.servers.set(serverName, client);

      console.log(`Connected to ${serverName}`);
      return client;

    } catch (error) {
      console.error(`Failed to connect to ${serverName}:`, error);
      throw error;
    }
  }

  async callTool(serverName, toolName, args) {
    const client = this.servers.get(serverName);
    if (!client) {
      throw new Error(`Server ${serverName} not connected`);
    }

    try {
      const result = await client.request(
        {
          method: 'tools/call',
          params: {
            name: toolName,
            arguments: args
          }
        }
      );

      return result;
    } catch (error) {
      console.error(`Tool call failed:`, error);
      throw error;
    }
  }

  async listTools(serverName) {
    const client = this.servers.get(serverName);
    if (!client) {
      throw new Error(`Server ${serverName} not connected`);
    }

    const response = await client.request({
      method: 'tools/list'
    });

    return response.tools;
  }
}

// Usage example
const mcpClient = new MCPCodexClient();

// Connect to multiple servers
await mcpClient.connectToServer('filesystem', 'mcp-server-filesystem');
await mcpClient.connectToServer('git', 'mcp-server-git');
await mcpClient.connectToServer('code-analysis', 'node', ['./code-analysis-server.js']);

// Use tools from different servers
const fileContent = await mcpClient.callTool('filesystem', 'read_file', {
  path: 'src/components/UserProfile.tsx'
});

const analysis = await mcpClient.callTool('code-analysis', 'analyze_code', {
  file_path: 'src/components/UserProfile.tsx',
  analysis_type: 'security'
});

Message Flow

Understanding the message flow between clients and servers is crucial for debugging and optimizing MCP applications:

message-flow.json
// MCP Message Flow Example

// 1. Client connects to server
{
  "jsonrpc": "2.0",
  "method": "initialize",
  "params": {
    "protocolVersion": "2024-11-05",
    "capabilities": { "tools": {} },
    "clientInfo": { "name": "MCPCodex", "version": "2.0.0" }
  },
  "id": 1
}

// 2. Server responds with capabilities
{
  "jsonrpc": "2.0",
  "result": {
    "protocolVersion": "2024-11-05",
    "capabilities": {
      "tools": { "listChanged": true },
      "resources": { "subscribe": true },
      "logging": {}
    },
    "serverInfo": { "name": "code-server", "version": "1.0.0" }
  },
  "id": 1
}

// 3. Client requests available tools
{
  "jsonrpc": "2.0",
  "method": "tools/list",
  "id": 2
}

// 4. Server lists available tools
{
  "jsonrpc": "2.0",
  "result": {
    "tools": [
      {
        "name": "generate_component",
        "description": "Generate React component",
        "inputSchema": {
          "type": "object",
          "properties": {
            "name": { "type": "string" },
            "props": { "type": "array" }
          }
        }
      }
    ]
  },
  "id": 2
}

// 5. Client calls a tool
{
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "name": "generate_component",
    "arguments": {
      "name": "UserCard",
      "props": ["user", "onEdit", "onDelete"]
    }
  },
  "id": 3
}

// 6. Server returns tool result
{
  "jsonrpc": "2.0",
  "result": {
    "content": [
      {
        "type": "text",
        "text": "Generated React component UserCard with props: user, onEdit, onDelete"
      },
      {
        "type": "resource",
        "resource": {
          "uri": "file://src/components/UserCard.tsx",
          "mimeType": "text/typescript"
        },
        "text": "import React from 'react';\n\ninterface UserCardProps {..."
      }
    ]
  },
  "id": 3
}

Transport Layers

Standard I/O (stdio)

The default transport for local MCP servers. Simple and efficient for single-machine deployments.

// Server runs as subprocess
spawn('node', ['server.js'])

HTTP/WebSocket

Network-based transport for distributed deployments and web applications.

// Remote server connection
connect('wss://api.example.com/mcp')

Custom Transports

Implement custom transport layers for specialized communication needs.

  • • Message queues (Redis, RabbitMQ)
  • • gRPC connections
  • • Custom protocols

Security Features

Built-in security features for all transport types.

  • • TLS encryption
  • • Authentication tokens
  • • Rate limiting
  • • Request validation

Performance & Scalability

Optimization Strategies

Connection Management

  • • Connection pooling for multiple servers
  • • Persistent connections with keep-alive
  • • Automatic reconnection on failures
  • • Load balancing across server instances

Caching Strategies

  • • Tool and resource metadata caching
  • • Response caching for expensive operations
  • • Context and state caching
  • • Intelligent cache invalidation

Parallel Processing

  • • Concurrent tool calls across servers
  • • Parallel resource fetching
  • • Asynchronous message handling
  • • Worker thread utilization

Resource Management

  • • Memory usage optimization
  • • Stream processing for large datasets
  • • Garbage collection tuning
  • • Resource cleanup and disposal

Next Steps

Building Custom MCP Servers?

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