The hardest part of building a useful agent is rarely the model — it's connecting the model to everything else: your database, your ticketing system, your files, your internal APIs. Historically every one of those connections was a bespoke integration, written and maintained by hand. The Model Context Protocol (MCP), introduced by Anthropic and now adopted broadly, exists to fix that.
The N-times-M problem
Say you have five AI applications and ten tools you want them to use. Without a standard, that's up to fifty separate integrations, each with its own auth, schema, and quirks. Add another tool and you write five more. This is the same integration sprawl that USB and language server protocols were invented to solve in their own domains.
MCP turns that N-times-M problem into N-plus-M. Each tool exposes itself once through an MCP server; each application speaks MCP once as a client. Any client can then use any server.
What an MCP server exposes
A server can offer three kinds of capability, and clients discover them at runtime:
- Tools — actions the model can invoke, like querying a database or creating a ticket.
- Resources — data the model can read, like files, records, or documentation.
- Prompts — reusable, parameterized templates a server can offer for common tasks.
Why discovery matters
Because capabilities are discovered at runtime rather than hard-coded, you can add a new tool to an agent without redeploying the agent. Stand up an MCP server, point the client at it, and the model can see and use the new capability immediately. For teams running many internal systems, that turns integration from a code change into a configuration change.
What it doesn't solve
MCP standardizes the plumbing, not the judgment. You still decide which servers an agent may connect to, what each is permitted to do, and how actions are audited. In practice we treat MCP servers like any other privileged dependency: scoped credentials, least privilege, and logging on every call. The protocol makes connection easy; governance is still your job.
The takeaway
MCP is best understood as a common port for agents. It won't make a bad agent good, but it removes an enormous amount of glue code and makes your tools reusable across every AI application you build. If you're maintaining a pile of one-off integrations, it's the first thing worth standardizing on.
