Building MCP Servers: A Developer's Complete Guide

What is MCP?

The Model Context Protocol (MCP) is a standard for connecting AI models to external tools and data sources. It's how Claude Code, and other AI agents, interact with your systems.

Why Build an MCP Server?

MCP servers let you:

• Give AI agents access to your internal tools
• Connect to databases, APIs, and services
• Create custom workflows for AI automation
• Control what data the AI can access

Basic Server Structure

import { Server } from "@modelcontextprotocol/sdk/server";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio";

const server = new Server({
  name: "my-mcp-server",
  version: "1.0.0",
});

// Define tools
server.setRequestHandler("tools/list", async () => ({
  tools: [
    {
      name: "get_user",
      description: "Fetch user by ID",
      inputSchema: {
        type: "object",
        properties: {
          userId: { type: "string" }
        },
        required: ["userId"]
      }
    }
  ]
}));

// Handle tool calls
server.setRequestHandler("tools/call", async (request) => {
  if (request.params.name === "get_user") {
    const user = await fetchUser(request.params.arguments.userId);
    return { content: [{ type: "text", text: JSON.stringify(user) }] };
  }
});

// Start server
const transport = new StdioServerTransport();
server.connect(transport);

Best Practices

1. Clear tool descriptions: The AI needs to understand when to use each tool
2. Strict input validation: Never trust AI-generated inputs
3. Rate limiting: Prevent runaway API calls
4. Logging: Track all tool invocations for debugging

Security Considerations

• Implement authentication for sensitive operations
• Use read-only access where possible
• Sanitize all inputs
• Set appropriate timeouts

Resources

• Official MCP Documentation
• Anthropic Cookbook

Want help building MCP integrations? Get in touch.