MCP Tutorial: Build AI Agents That Actually Do Things (2026 Guide)

mkdir my-mcp-agent
cd my-mcp-agent
npm init -y
npm install @anthropic-ai/sdk @modelcontextprotocol/sdk

// weather-server.js
import { MCPServer } from '@modelcontextprotocol/sdk';
 
const server = new MCPServer({
  name: 'weather-server',
  version: '1.0.0'
});
 
// Define a tool the AI can use
server.addTool({
  name: 'get_weather',
  description: 'Get current weather for a city',
  parameters: {
    type: 'object',
    properties: {
      city: { type: 'string', description: 'City name' }
    },
    required: ['city']
  },
  handler: async ({ city }) => {
    // In production, call a real weather API
    const weather = {
      city,
      temp: Math.floor(Math.random() * 30) + 40,
      condition: ['sunny', 'cloudy', 'rainy'][Math.floor(Math.random() * 3)]
    };
    return JSON.stringify(weather);
  }
});
 
server.start();
console.log('Weather MCP server running...');

// agent.js
import Anthropic from '@anthropic-ai/sdk';
 
const client = new Anthropic();
 
async function runAgent(userMessage) {
  const response = await client.messages.create({
    model: 'claude-sonnet-4-20250514',
    max_tokens: 1024,
    tools: [
      {
        name: 'get_weather',
        description: 'Get current weather for a city',
        input_schema: {
          type: 'object',
          properties: {
            city: { type: 'string', description: 'City name' }
          },
          required: ['city']
        }
      }
    ],
    messages: [{ role: 'user', content: userMessage }]
  });
 
  // Handle tool calls
  for (const block of response.content) {
    if (block.type === 'tool_use') {
      console.log(`AI wants to use: ${block.name}`);
      console.log(`With input: ${JSON.stringify(block.input)}`);
      // Call your MCP server here and return results
    }
  }
 
  return response;
}
 
// Test it
runAgent("What's the weather like in Denver?");

git clone https://github.com/you/my-mcp-agent
cd my-mcp-agent
npm install
pm2 start agent.js --name mcp-agent