Research note: Model specifications and benchmark results are time-bound. Check the dated primary sources below before using them for a technical or purchasing decision.

Prior to the Model Context Protocol (MCP), every AI tool integration was an isolated proprietary silo: OpenAI plugins, LangChain tools, custom JSON endpoints. MCP standardizes the communication layer between AI models and external data sources as the universal USB-C for agents.

1. MCP Client-Server Architecture

// Example: Production MCP Server in TypeScript
import { Server } from '@modelcontextprotocol/sdk/server/index.js';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
import { CallToolRequestSchema, ListToolsRequestSchema } from '@modelcontextprotocol/sdk/types.js';

const server = new Server({ name: 'tokenscache-mcp', version: '1.0.0' }, { capabilities: { tools: {} } });

server.setRequestHandler(ListToolsRequestSchema, async () => ({
  tools: [
    {
      name: 'get_cache_stats',
      description: 'Returns real-time token savings and cache hit metrics.',
      inputSchema: { type: 'object', properties: {} }
    }
  ]
}));

const transport = new StdioServerTransport();
await server.connect(transport);

Sources & Standards