MCP Servers Explained: The Protocol Powering AI Agents

What is Model Context Protocol (MCP)?

Model Context Protocol (MCP) is an open standard developed by Anthropic that revolutionizes how AI models interact with external systems. Released in late 2024 and rapidly adopted throughout 2025-2026, MCP provides a universal interface for AI to access files, databases, APIs, and tools.

Before MCP, every AI integration required custom code. Want Claude to read your files? Write custom code. Want it to query a database? More custom code. Want GPT to do the same things? Rewrite everything.

MCP changes this by providing a standardized protocol that any AI can speak. One integration works everywhere.

Why MCP Matters

MCP Architecture

MCP follows a client-server architecture:

Components

1. MCP Hosts (Clients)

   • AI applications like Claude Desktop, Open WebUI
   • Connect to multiple MCP servers simultaneously
   • Manage user approvals and security

2. MCP Servers

   • Expose specific capabilities (files, databases, APIs)
   • Run as separate processes for isolation
   • Can be local or remote

3. Transport Layer

   • stdio: Local servers via standard input/output
   • SSE: Remote servers via HTTP Server-Sent Events

Protocol Flow

User Request → AI Model → MCP Client → MCP Server → External System
                   ↑                         ↓
                   ←─────── Response ────────←

Top MCP Servers (2026)

Official Anthropic Servers

Popular Community Servers

Setting Up MCP with Claude Desktop

Step 1: Locate Config File

macOS:

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

Windows:

%APPDATA%\Claude\claude_desktop_config.json

Step 2: Add MCP Servers

Create or edit the config file:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@anthropic-ai/mcp-server-filesystem",
        "/Users/yourname/Documents",
        "/Users/yourname/Projects"
      ]
    },
    "github": {
      "command": "npx",
      "args": ["-y", "@anthropic-ai/mcp-server-github"],
      "env": {
        "GITHUB_TOKEN": "ghp_your_token_here"
      }
    },
    "sqlite": {
      "command": "npx",
      "args": [
        "-y",
        "@anthropic-ai/mcp-server-sqlite",
        "/path/to/your/database.db"
      ]
    }
  }
}

Step 3: Restart Claude Desktop

After saving the config, restart Claude Desktop. You'll see available tools in the interface.

Step 4: Test the Connection

Ask Claude:

• "What files are in my Documents folder?"
• "Show me my recent GitHub issues"
• "Query the users table in my database"

Building a Custom MCP Server

Here's a simple MCP server in TypeScript:

Install the SDK

npm init -y
npm install @modelcontextprotocol/sdk

Create the Server

// server.ts
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: {
    tools: {},
    resources: {},
  }
});

// Define a tool
server.setRequestHandler('tools/list', async () => ({
  tools: [{
    name: 'get_weather',
    description: 'Get current weather for a city',
    inputSchema: {
      type: 'object',
      properties: {
        city: { type: 'string', description: 'City name' }
      },
      required: ['city']
    }
  }]
}));

// Handle tool calls
server.setRequestHandler('tools/call', async (request) => {
  if (request.params.name === 'get_weather') {
    const city = request.params.arguments.city;
    // Your weather API logic here
    return {
      content: [{
        type: 'text',
        text: `Weather in ${city}: 72°F, Sunny`
      }]
    };
  }
});

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

Register in Claude Desktop

{
  "mcpServers": {
    "my-weather": {
      "command": "npx",
      "args": ["tsx", "/path/to/server.ts"]
    }
  }
}

MCP with Local AI Models

Using MCP with Ollama

While Ollama doesn't natively support MCP, you can use compatible frontends:

Open WebUI with MCP:

# Install Open WebUI with MCP support
docker run -d -p 3000:8080 \
  -e MCP_SERVERS='{"filesystem": {"command": "npx", "args": ["-y", "@anthropic-ai/mcp-server-filesystem", "/data"]}}' \
  --add-host=host.docker.internal:host-gateway \
  -v open-webui:/app/backend/data \
  ghcr.io/open-webui/open-webui:main

LangChain MCP Integration:

from langchain_mcp import MCPToolkit
from langchain_community.llms import Ollama

# Connect to MCP servers
toolkit = MCPToolkit(servers={
    "filesystem": {
        "command": "npx",
        "args": ["-y", "@anthropic-ai/mcp-server-filesystem", "/data"]
    }
})

# Use with local Ollama model
llm = Ollama(model="llama3.1:70b")
tools = toolkit.get_tools()

Real-World MCP Use Cases

1. Code Assistant with Repository Access

Configure GitHub MCP server to let AI browse repos, read code, and create PRs.

2. Database Query Assistant

SQLite or PostgreSQL MCP servers enable natural language database queries.

3. Document Analysis Pipeline

Filesystem MCP server reads documents while AI processes and summarizes.

4. DevOps Automation

Docker and Kubernetes MCP servers enable AI-driven infrastructure management.

5. Knowledge Management

Memory MCP server persists context across conversations for long-term projects.

Security Best Practices

Principle of Least Privilege

Only grant MCP servers access to necessary directories and resources.

{
  "filesystem": {
    "command": "npx",
    "args": [
      "-y",
      "@anthropic-ai/mcp-server-filesystem",
      "/specific/project/folder"  // Not entire home directory
    ]
  }
}

Environment Variable Security

Never commit tokens to config files. Use environment variables:

{
  "github": {
    "command": "npx",
    "args": ["-y", "@anthropic-ai/mcp-server-github"],
    "env": {
      "GITHUB_TOKEN": "${GITHUB_TOKEN}"  // Read from environment
    }
  }
}

Audit Server Code

Review community MCP servers before use, especially those requiring sensitive access.

MCP vs Alternatives

The Future of MCP

MCP adoption accelerated in 2025-2026:

• OpenAI announced MCP support for ChatGPT and API
• Google integrated MCP into Gemini ecosystem
• Enterprise adoption for secure AI tool integration
• Multimodal MCP for image and audio resources
• MCP Hub marketplace for discovering servers

The protocol is becoming the de facto standard for AI-tool integration.

Key Takeaways

1. MCP standardizes AI-tool integration with a universal protocol
2. Official servers cover common use cases (files, GitHub, databases)
3. Easy to setup with Claude Desktop config file
4. Build custom servers in <100 lines of TypeScript or Python
5. Works with local AI through compatible clients
6. Security-first design with process isolation and approval flows

Next Steps

1. Set up local AI agents using MCP tools
2. Configure RAG pipelines with MCP filesystem access
3. Explore DeepSeek R1 for complex reasoning with tools
4. Browse AI models that work well with MCP integrations

---

MCP represents the future of AI integration—open, standardized, and secure. Whether you're building AI agents, automating workflows, or creating custom tools, MCP provides the foundation for connected AI experiences.