Model Context Protocol (MCP) servers are transforming how AI assistants interact with the real world. Instead of being limited to text in and text out, MCP-enabled AI can read files, query databases, call APIs, and execute code. Here's how to get started.
What Is MCP?#
MCP (Model Context Protocol) is a standardized way for AI models to interact with external tools and data sources. Think of it as a plugin system for AI assistants.
Without MCP, an AI can only:
- Process text you provide
- Generate text responses
With MCP, an AI can:
- Read and write files
- Query databases
- Call external APIs
- Execute code
- Access specialized tools
Why MCP Matters for Developers#
Traditional AI coding assistants work with whatever context you manually provide. MCP servers let AI assistants:
- Browse your codebase directly instead of relying on copy-paste
- Run tests to verify suggestions
- Query documentation for accurate API usage
- Access databases to understand data structures
- Use specialized tools built for specific workflows
Setting Up Your First MCP Server#
Step 1: Choose Your MCP Client#
MCP servers work with compatible clients like Claude Code. Check that your AI tool supports MCP.
Step 2: Configure the Server#
Create an MCP configuration file (typically .mcp.json in your project root):
1{
2 "mcpServers": {
3 "filesystem": {
4 "command": "npx",
5 "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/project"]
6 },
7 "database": {
8 "command": "npx",
9 "args": ["-y", "@modelcontextprotocol/server-postgres"],
10 "env": {
11 "DATABASE_URL": "${DATABASE_URL}"
12 }
13 }
14 }
15}Step 3: Verify Connection#
Start your AI assistant and verify the MCP servers are connected. Most clients will show available tools and their status.
Common MCP Servers#
Filesystem Server#
Read, write, and navigate files in your project:
1{
2 "filesystem": {
3 "command": "npx",
4 "args": ["-y", "@modelcontextprotocol/server-filesystem", "."]
5 }
6}Database Servers#
Query PostgreSQL, MySQL, or SQLite databases:
1{
2 "postgres": {
3 "command": "npx",
4 "args": ["-y", "@modelcontextprotocol/server-postgres"],
5 "env": {
6 "DATABASE_URL": "postgresql://..."
7 }
8 }
9}Git Server#
Access repository history, branches, and diffs:
1{
2 "git": {
3 "command": "npx",
4 "args": ["-y", "@modelcontextprotocol/server-git", "."]
5 }
6}Building Custom MCP Servers#
For specialized needs, you can build your own MCP servers. Here's a simple example:
1import { Server } from '@modelcontextprotocol/sdk/server';
2import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio';
3
4const server = new Server({
5 name: 'my-custom-server',
6 version: '1.0.0'
7});
8
9// Define a tool
10server.tool('analyze-dependencies', {
11 description: 'Analyze project dependencies for security issues',
12 inputSchema: {
13 type: 'object',
14 properties: {
15 path: { type: 'string', description: 'Path to package.json' }
16 }
17 },
18 handler: async ({ path }) => {
19 // Your implementation here
20 return { vulnerabilities: [], recommendations: [] };
21 }
22});
23
24// Start the server
25const transport = new StdioServerTransport();
26server.connect(transport);Best Practices#
Security First#
MCP servers have access to your system. Follow these guidelines:
- Limit file system access to project directories
- Use read-only access when possible
- Never commit credentials in configuration files
- Review server code before running untrusted servers
Performance Considerations#
- MCP calls add latency—use them judiciously
- Cache results when appropriate
- Batch related operations when possible
Debugging Tips#
- Check server logs for errors
- Verify environment variables are set correctly
- Test servers independently before integrating
The MCP Ecosystem#
The MCP ecosystem is growing rapidly:
- Official servers: Filesystem, Git, databases, browsers
- Community servers: Slack, Jira, Notion, custom APIs
- Specialized tools: Code analysis, testing, deployment
Conclusion#
MCP servers bridge the gap between AI assistants and the real world. They transform AI from a conversation partner into an active collaborator that can see your code, understand your data, and take meaningful actions.
Start with basic servers like filesystem access, then expand as you discover more powerful workflows. The combination of AI intelligence and real-world tools is where the magic happens.