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Model Context Protocol (MCP) Servers Overview

What is an MCP Server?

The Model Context Protocol (MCP) is an open standard that enables AI assistants like GitHub Copilot, Claude, and other LLMs to securely connect to external data sources and tools. An MCP server is a program that implements this protocol to expose specific capabilities, data, or services to AI assistants.

Think of MCP servers as specialized plugins or extensions that give AI assistants superpowers:

  • Access to databases, APIs, and file systems
  • Integration with business tools (Slack, Jira, GitHub, etc.)
  • Custom domain-specific operations
  • Real-time data retrieval and manipulation

Key Concepts

MCP Architecture: 

AI Assistant (Client) <--> MCP Server <--> Resources/Tools/Data

Core Components:

  • Resources: Data or content the AI can read (files, database records, API responses)
  • Tools: Actions the AI can perform (run queries, create records, execute commands)
  • Prompts: Templated interactions or workflows
  • Sampling: Ability for servers to request AI completions

Why Create an MCP Server?

Use Cases for Building Custom MCP Servers

  1. Enterprise Data Access

    • Connect AI to internal databases, data warehouses, or document repositories
    • Enable AI to query proprietary data sources securely
    • Provide context from company wikis, knowledge bases, or CRMs

  2. Workflow Automation

    • Create custom tools for domain-specific tasks
    • Automate complex multi-step processes
    • Integrate with existing business systems (ERP, monitoring tools, etc.)

  3. Specialized Domain Knowledge

    • Expose industry-specific APIs and data
    • Provide access to scientific instruments or simulation systems
    • Enable AI to interact with IoT devices or industrial equipment

  4. Security and Compliance

    • Control exactly what data AI assistants can access
    • Implement custom authentication and authorization
    • Audit and log all AI interactions with sensitive systems

  5. Development Tools

    • Create specialized debugging or testing tools
    • Integrate with CI/CD pipelines
    • Provide access to deployment systems and infrastructure

Benefits

  • Standardized Interface: One implementation works across multiple AI assistants
  • Security: Servers run locally or in controlled environments
  • Modularity: Mix and match different MCP servers for different capabilities
  • Reusability: Share servers across teams and organizations

How to Use Existing MCP Servers

Official Servers (by Anthropic):

  • @modelcontextprotocol/server-filesystem - File system access
  • @modelcontextprotocol/server-github - GitHub integration
  • @modelcontextprotocol/server-postgres - PostgreSQL database access
  • @modelcontextprotocol/server-sqlite - SQLite database access
  • @modelcontextprotocol/server-google-drive - Google Drive integration
  • @modelcontextprotocol/server-slack - Slack integration

Azure & Microsoft Ecosystem:

  • azure-mcp - Azure resource management and operations
  • Various Azure service-specific servers (Cosmos DB, App Service, etc.)

Community Servers:

Installing MCP Servers

For VS Code / GitHub Copilot

  1. Install the MCP server package (typically via npm, pip, or binary):

    ```bash
# Node.js-based server
npm install -g @modelcontextprotocol/server-filesystem

# Python-based server
pip install mcp-server-example
```

  2. Configure in VS Code settings (settings.json):

    ```json
{
"mcp.servers": {
"filesystem": {
    "command": "npx",
    "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/allowed/directory"]
},
"postgres": {
    "command": "npx",
    "args": ["-y", "@modelcontextprotocol/server-postgres"],
    "env": {
    "POSTGRES_CONNECTION_STRING": "postgresql://user:pass@localhost/db"
    }
}
}
}
```

  3. Reload VS Code to activate the servers

For Claude Desktop

Edit the Claude configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/username/Documents"]
    },
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "your-token-here"
      }
    }
  }
}

Using MCP Servers in AI Conversations

Once configured, you can naturally interact with MCP server capabilities:

Example with filesystem server: 

You: "Read the contents of config.json in my project"
AI: [Uses filesystem server to read the file]

You: "Create a new file called test.py with a hello world script"
AI: [Uses filesystem server to create the file]

Example with database server: 

You: "Show me all users created in the last 30 days"
AI: [Queries the database via MCP server]

You: "Create a new user record for john.doe@example.com"
AI: [Executes INSERT via MCP server]

Best Practices

  1. Principle of Least Privilege: Configure servers with minimal necessary permissions
  2. Use Environment Variables: Store credentials and sensitive data in env vars, not config
  3. Start Small: Begin with one or two servers and expand as needed
  4. Review Logs: Monitor MCP server activity for debugging and security
  5. Update Regularly: Keep server packages updated for security and features

Creating Your Own MCP Server

Quick Start

TypeScript/Node.js:

# Use the official template
npx @modelcontextprotocol/create-server my-server

cd my-server
npm install
npm run build

Python:

# Install MCP SDK
pip install mcp

# Create server file
# (See official Python examples in MCP documentation)

Basic Server Structure

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

const server = new Server({
  name: "my-custom-server",
  version: "1.0.0",
}, {
  capabilities: {
    resources: {},
    tools: {},
  },
});

// Define a tool
server.setRequestHandler("tools/call", async (request) => {
  if (request.params.name === "my_tool") {
    // Implement tool logic
    return {
      content: [{ type: "text", text: "Tool executed successfully" }],
    };
  }
});

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

Resources