What is MCP?
The Model Context Protocol (MCP) is an open standard created by Anthropic that allows language models to interact with external tools and services in a structured way. It defines how an AI agent discovers available capabilities, sends requests and receives results.
In essence, MCP solves the integration problem between LLMs and external systems in a standardized way, eliminating the need to write ad-hoc connectors for each tool.
MCP architecture
MCP follows a client-server model:
- MCP Host: The application running the AI agent (Claude Desktop, OpenCode, Pi Coding Agent)
- MCP Client: The connector inside the host that talks to the servers
- MCP Server: Exposes tools, resources and prompts that the model can consume
Communication uses JSON-RPC 2.0 over stdin/stdout (local transport) or HTTP/SSE (remote transport).
Real business applications
1. Customer service agent with CRM access
An MCP server exposes CRM operations — check orders, create tickets, verify account status. The AI agent accesses this data in real time during the conversation with the customer, without complex integrations.
2. Database automation
MCP servers exposing SQL queries, migrations and backups. The agent can analyze data, generate reports and run maintenance operations using natural language.
3. Multi-tool workflows
An agent with access to several MCP servers — email, calendar, CRM, ERP — can coordinate complex tasks: "Review pending orders, send a summary by email to the team and schedule a follow-up meeting".
Building an MCP server
Minimal Python example with the official SDK:
from mcp.server import Server, NotificationOptions
from mcp.server.models import InitializationCapabilities
server = Server("my-tool")
@server.list_tools()
async def list_tools():
return [{"name": "check_order", "description": "Looks up an order by ID"}]
@server.call_tool()
async def call_tool(name: str, arguments: dict):
if name == "check_order":
order_id = arguments["id"]
# Real lookup logic here
return {"status": "shipped", "id": order_id}Why MCP matters
Before MCP, every integration between AI and tools required specific code. MCP standardizes the interface, allowing any compatible agent to use any compatible tool without modifications.
This drastically reduces the development time of business agents and enables an ecosystem of reusable tools. In my experience, moving from ad-hoc integrations to MCP cut agent development time by 60-70%.
Production use cases
- Automated customer service: Agents that query knowledge bases, CRMs and ticketing systems in real time
- Intelligent DevOps: Agents that run commands, analyze logs and deploy changes with human supervision
- Conversational data analysis: Natural-language queries over business databases
- Booking automation: Agents that manage availability, pricing and confirmations