What Is MCP (Model Context Protocol)? Practical Guide
What is MCP? A practical guide to the Model Context Protocol: how it works, servers vs clients, security, real business use cases and how to start.
MCP, the Model Context Protocol, is an open standard that defines how AI assistants discover and use external tools and data. Think of it as a universal port for AI: instead of integrating your application separately with every assistant, you expose one MCP server and every compatible assistant can use it.
How MCP works
An MCP client lives inside the AI assistant. An MCP server lives next to your application and publishes a list of tools (actions), resources (data) and prompts. The assistant reads that list, decides which tool to call based on the user's request, and the server executes it with the user's permissions.
Key concepts
- Tools: typed actions such as create_invoice or list_orders.
- Resources: read-only data the assistant can load as context.
- Transport: Streamable HTTP for remote servers, stdio for local ones.
- Authorization: OAuth 2.1 so the assistant acts as a real, authenticated user.
Business use cases
- Sales teams managing leads and quotes from their AI assistant.
- Support teams triaging conversations and creating tickets.
- Operations updating inventory and checking delayed orders.
- Finance generating cash reports and exporting to accounting.
Security essentials
Never expose service-level keys. Authenticate each user, enforce permissions on every query, mark destructive tools, require approval where needed and log every call. Done right, MCP is safer than ad-hoc integrations because every action is explicit and auditable.
How to get started
List the actions your users repeat most, design them as typed tools, and deploy a first MCP server on top of your existing backend. Implementation takes 24 hours — see custom MCP server development.
Frequently asked questions
Is MCP only for developers?
No. Developers build MCP servers, but the people who benefit are end users who can operate software by asking their AI assistant.
What is the difference between an MCP server and an API?
An API is designed for developers; an MCP server is designed for AI assistants, with tool discovery, typed schemas and per-user authorization.
Who uses MCP?
A growing number of AI assistants, IDEs and agent frameworks support MCP, which makes one server reusable across many clients.
Assessment at no cost · Implementation takes 24 hours.
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Contacto: jp@rexlabs.cl | +56 9 4548 6320 | Santiago, Chile