Model Context Protocol (MCP):
The USB-C of AI Tool Integration

Model Context Protocol (MCP) is an open standard developed by Anthropic that defines how AI models connect to external tools and data sources. Think of it as USB-C for AI — a universal connector that lets any AI application work with any tool or service through a common interface.

The Problem MCP Solves

Before MCP, every AI application needed custom integrations for each tool:

• App A implements Google Drive integration → can't be reused by App B
• App B implements Slack integration → different interface than App A's
• 100 AI apps × 100 tools = 10,000 custom integrations

With MCP, tools expose a standard interface. AI apps implement the MCP client protocol once. Result: any MCP-compatible app works with any MCP-compatible tool — the "N × M problem" becomes "N + M."

MCP Architecture

MCP has three layers:

• MCP Host: The AI application (Claude Desktop, Claude Code, your custom app)
• MCP Client: Built into the host — manages connections to servers
• MCP Server: Exposes tools, resources, and prompts via the MCP protocol

Communication happens over:

• stdio: Local servers — MCP client spawns a subprocess and communicates via stdin/stdout
• SSE (Server-Sent Events): Remote servers — HTTP with persistent event stream

MCP Primitives: Tools, Resources & Prompts

Building an MCP Server in Python

# Install: pip install mcp
from mcp.server.fastmcp import FastMCP
from typing import Optional
import httpx
import json

# Create the MCP server
mcp = FastMCP("my-company-tools")

@mcp.tool()
async def get_customer_info(customer_id: str) -> str:
    """
    Get detailed information about a customer by their ID.

    Args:
        customer_id: The unique customer identifier (e.g., 'CUST-12345')

    Returns:
        JSON string with customer name, email, plan, and account status
    """
    # In production, query your actual database
    # Here we simulate it
    customers = {
        "CUST-001": {
            "name": "Alice Johnson",
            "email": "alice@example.com",
            "plan": "Enterprise",
            "status": "active",
            "mrr": 2499.00
        }
    }
    customer = customers.get(customer_id)
    if not customer:
        return json.dumps({"error": f"Customer {customer_id} not found"})
    return json.dumps(customer)

@mcp.tool()
async def search_knowledge_base(query: str, limit: int = 5) -> str:
    """
    Search the company knowledge base for relevant articles.

    Args:
        query: Search query in natural language
        limit: Maximum number of results to return (1-20)
    """
    # Connect to your actual search backend
    async with httpx.AsyncClient() as client:
        response = await client.get(
            "https://your-kb-api.internal/search",
            params={"q": query, "limit": min(limit, 20)},
            headers={"Authorization": "Bearer your-internal-token"}
        )
        results = response.json()
    return json.dumps(results)

@mcp.tool()
async def create_support_ticket(
    customer_id: str,
    subject: str,
    description: str,
    priority: str = "normal"
) -> str:
    """
    Create a support ticket for a customer.

    Args:
        customer_id: Customer identifier
        subject: Brief subject line (max 100 chars)
        description: Detailed description of the issue
        priority: Ticket priority - 'low', 'normal', 'high', 'urgent'
    """
    if priority not in ("low", "normal", "high", "urgent"):
        return json.dumps({"error": "Invalid priority. Use: low, normal, high, urgent"})

    # Create ticket in your ticketing system
    ticket = {
        "ticket_id": "TKT-98765",
        "customer_id": customer_id,
        "subject": subject,
        "priority": priority,
        "status": "open",
        "created_at": "2026-05-30T10:00:00Z"
    }
    return json.dumps({"success": True, "ticket": ticket})

# Expose a resource (readable data)
@mcp.resource("config://company/support-policies")
async def get_support_policies() -> str:
    """Read-only access to support policies."""
    return """
    SUPPORT POLICIES (v2.1, updated 2026-05):
    - Response SLA: 4h (normal), 1h (high), 15min (urgent)
    - Refund policy: Full refund within 30 days, pro-rated after
    - Escalation: VP of Support for enterprise customers
    """

# Run the server (stdio mode for Claude Desktop)
if __name__ == "__main__":
    mcp.run(transport="stdio")

MCP Server Configuration

Configure Claude Desktop or Claude Code to use your server by adding it to the MCP config file:

# claude_desktop_config.json (Mac: ~/Library/Application Support/Claude/)
{
  "mcpServers": {
    "my-company-tools": {
      "command": "python",
      "args": ["/path/to/your/mcp_server.py"],
      "env": {
        "DATABASE_URL": "postgresql://...",
        "API_KEY": "your-internal-api-key"
      }
    },

    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem",
               "/Users/you/Documents"]
    },

    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "ghp_..."
      }
    }
  }
}

Using MCP from Your Own Application

# Connect to MCP servers from your Python application
import asyncio
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

async def use_mcp_tools():
    server_params = StdioServerParameters(
        command="python",
        args=["mcp_server.py"]
    )

    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            # Initialize
            await session.initialize()

            # List available tools
            tools = await session.list_tools()
            print(f"Available tools: {[t.name for t in tools.tools]}")

            # Call a tool
            result = await session.call_tool(
                "get_customer_info",
                {"customer_id": "CUST-001"}
            )
            print(f"Tool result: {result.content[0].text}")

            # Read a resource
            resource = await session.read_resource(
                "config://company/support-policies"
            )
            print(f"Resource: {resource.contents[0].text}")

asyncio.run(use_mcp_tools())

Popular Pre-Built MCP Servers

• filesystem — read/write local files with access controls
• github — search repos, read files, create issues and PRs
• postgres — query PostgreSQL databases safely
• brave-search — web and local search
• slack — read channels, search messages, post replies
• google-drive — read and search Drive files
• puppeteer — browser automation for web interactions

Install via: npx -y @modelcontextprotocol/server-github

Key Takeaways

• MCP standardizes how AI models connect to tools — write once, use with any MCP-compatible AI
• Three primitives: Tools (callable functions), Resources (readable data), Prompts (templates)
• FastMCP makes building Python MCP servers extremely simple — just @mcp.tool() decorators
• Configure Claude Desktop/Code to use your servers via the JSON config file
• Dozens of pre-built MCP servers available for common services (GitHub, Slack, databases)
• MCP is Anthropic's open standard — now supported by VS Code, Cursor, and growing ecosystem