Getting Started with MCP Server

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Title:

Getting Started with MCP Server: A Complete Guide for Developers

Slug:

getting-started-mcp-server

Excerpt:

Learn how to build your first MCP server and connect AI models to real tools. This beginner-friendly guide walks you through setup, implementation, and best practices.

SEO Title:

MCP Server Tutorial: Build AI Agents That Connect to Real Tools (2026)

SEO Description:

Complete MCP server tutorial for developers. Learn how to connect Claude, GPT-4, and other AI models to databases, APIs, and services using the Model Context Protocol.

Content:

What is an MCP Server?

MCP (Model Context Protocol) servers are the bridge between AI models and real-world tools. Instead of AI being limited to text generation, MCP servers give AI the ability to interact with databases, APIs, file systems, and external services.

Think of it this way: without MCP, Claude can tell you how to query a database. With MCP, Claude can actually query your database and return results.

Why MCP Matters

Traditional AI integrations require custom code for every tool you want to connect. MCP standardizes this process:

  • One protocol for all connections
  • AI models can discover available tools automatically
  • Developers write less boilerplate code
  • Users get more powerful AI agents

Your First MCP Server

Let's build a simple MCP server that connects to a PostgreSQL database.

Prerequisites:

  • Node.js 18+ or Python 3.10+
  • Basic understanding of APIs
  • A database or API you want to connect

Step 1: Install the MCP SDK

For Node.js:

bash

npm install @modelcontextprotocol/sdk

For Python:

bash

pip install mcp

Step 2: Define Your Tools

MCP servers expose "tools" that AI models can use. Here's a simple database query tool:

typescript

import { MCPServer } from '@modelcontextprotocol/sdk'; const server = new MCPServer({ name: 'my-database-server', version: '1.0.0' }); server.addTool({ name: 'query_database', description: 'Execute SQL queries on the production database', parameters: { query: { type: 'string', description: 'SQL query to execute' } }, handler: async ({ query }) => { // Your database logic here const results = await db.query(query); return results; } });

Step 3: Start Your Server

typescript

server.listen(3000); console.log('MCP server running on port 3000');

Step 4: Connect Your AI Model

Configure Claude Desktop or your AI application to connect to your MCP server:

json

{ "mcpServers": { "my-database": { "url": "http://localhost:3000" } } }

Best Practices

  1. Authentication: Always secure your MCP servers with API keys or OAuth
  2. Rate Limiting: Prevent abuse by limiting requests per minute
  3. Error Handling: Return clear error messages the AI can understand
  4. Documentation: Describe your tools clearly so AI knows when to use them
  5. Monitoring: Log all requests for debugging and compliance

Common Use Cases

  • Database Access: Let AI query and analyze your data
  • API Integration: Connect to Stripe, SendGrid, Twilio, etc.
  • File Operations: Read, write, and manage files
  • Internal Tools: Access company wikis, documentation, ticketing systems

Production Considerations

Before deploying to production:

  • Implement proper authentication and authorization
  • Add request validation and sanitization
  • Set up monitoring and alerting
  • Test thoroughly with edge cases
  • Document all available tools

Next Steps

Now that you understand the basics, explore:

  • Building multi-tool MCP servers
  • Connecting multiple data sources
  • Implementing custom authentication
  • Optimizing for performance
  • Deploying to production

Need Help?

Join our community or check out the full documentation for advanced examples and troubleshooting.

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