Building High-Performance Model Context Protocol (MCP) Servers in TypeScript & Go

The Model Context Protocol (MCP) has rapidly emerged as the universal standard for linking Large Language Models (LLMs) to external data sources, enterprise tools, and local development environments. By standardizing how clients (such as Claude Code, Cursor, or custom agent runtimes) discover and execute tools, MCP eliminates brittle point-to-point integrations.

Figure 1: High-throughput MCP server handling multiplexed JSON-RPC requests across local and cloud resources.

1. Architectural Foundations of MCP

MCP follows a client-server paradigm built on JSON-RPC 2.0. The protocol abstracts three primary capabilities:

• Resources: File-like data streams that can be read by clients (e.g. log files, DB schemas, system metrics).
• Prompts: Pre-templated system instructions exposed dynamically by servers.
• Tools: Executable functions that LLMs can invoke with structured input schemas.

2. Building an MCP Server in TypeScript

Using the official @modelcontextprotocol/sdk, you can create a production-ready stdio server in under 50 lines of code:

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: "stack-hive-analytics", version: "1.0.0" },
  { capabilities: { tools: {} } }
);

server.setRequestHandler(ListToolsRequestSchema, async () => ({
  tools: [
    {
      name: "query_database_stats",
      description: "Returns real-time query performance and latency metrics",
      inputSchema: {
        type: "object",
        properties: {
          timeframe: { type: "string", enum: ["1h", "24h", "7d"] }
        },
        required: ["timeframe"]
      }
    }
  ]
}));

const transport = new StdioServerTransport();
await server.connect(transport);
console.error("MCP Server listening on stdio...");