{ TECH BLOG }

The Model Context Protocol (MCP) is an open standard that defines how AI models and agents communicate with external tools, data sources, and services. Think of it as the USB-C of AI integrations — a universal connector that lets any AI model work with any tool through a standardized interface.

Introduced by Anthropic and rapidly adopted across the industry, MCP solves the N×M integration problem: instead of building custom integrations between every AI model and every tool, both sides implement the MCP standard and interoperability comes for free.

MCP follows a client-server architecture:

• MCP Host — The AI application (e.g., Claude, ChatGPT, custom agents) that initiates tool calls
• MCP Client — Manages the connection between the host and servers
• MCP Server — Exposes tools, resources, and prompts to AI models. Each server wraps a specific capability (database access, API calls, file operations, etc.)

// Example: Simple MCP Server in Node.js
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";

const server = new McpServer({ name: "my-tools", version: "1.0.0" });

server.tool("get_weather", { city: "string" }, async ({ city }) => {
  const data = await fetchWeather(city);
  return { content: [{ type: "text", text: JSON.stringify(data) }] };
});

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

• Tools — Functions the AI can invoke (e.g., query a database, send an email, create a ticket)
• Resources — Data the AI can read (e.g., file contents, API responses, documentation)
• Prompts — Reusable prompt templates for common workflows
• Sampling — Allows servers to request LLM completions from the client

MCP enables powerful enterprise integrations:

• Connect AI assistants to internal databases, CRMs, and ticketing systems
• Build AI-powered DevOps bots that can query logs, deploy code, and manage infrastructure
• Create knowledge management systems where AI can search and update documentation
• Enable AI agents to orchestrate complex multi-step workflows across multiple services

Getting started with MCP is straightforward. The official SDKs are available for Python, TypeScript/Node.js, and other languages. The key is to identify the tools and data sources your AI needs access to, then wrap them in MCP server endpoints. Start with read-only tools, add proper authentication, and gradually expand to write operations as you build trust in the system.

I recently built an MCP server that connects Claude to our company's PostgreSQL database, Jira API, and GitHub repositories. Here's a simplified version of the database tool:

from mcp.server import Server
from mcp.types import Tool, TextContent
import asyncpg

server = Server("company-db")

@server.list_tools()
async def list_tools():
    return [
        Tool(
            name="query_customers",
            description="Query customer data from PostgreSQL",
            inputSchema={
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "SQL SELECT query"}
                },
                "required": ["query"]
            }
        )
    ]

@server.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "query_customers":
        # Validate query is SELECT only (security)
        query = arguments["query"].strip().upper()
        if not query.startswith("SELECT"):
            return [TextContent(type="text", text="Error: Only SELECT queries allowed")]
        
        # Execute query with connection pooling
        conn = await asyncpg.connect(DATABASE_URL)
        try:
            rows = await conn.fetch(arguments["query"])
            result = [dict(row) for row in rows]
            return [TextContent(type="text", text=json.dumps(result, indent=2))]
        finally:
            await conn.close()

The AI can now query our customer database naturally: "Show me all customers who signed up in the last 30 days with revenue > $10K". The MCP server translates this into SQL, executes it safely, and returns results. This eliminated hours of manual data pulls per week.

When exposing enterprise systems to AI, security is paramount:

• Input Validation — Sanitize all AI-generated inputs before executing (SQL injection, command injection risks)
• Least Privilege — MCP servers should run with minimal permissions (read-only DB users, scoped API tokens)
• Rate Limiting — Prevent runaway AI agents from overwhelming your systems
• Audit Logging — Log every tool invocation with user context for compliance and debugging
• Approval Workflows — For destructive operations, require human approval before execution