If you’ve built an AI agent that connects to external tools, you’ve written the same glue code a hundred times: a function that takes model output, parses the tool call, calls the right API, formats the result, and feeds it back into context.
It works. It’s also completely custom every time, untestable in isolation, and breaks when the model changes its output format.
MCP — the Model Context Protocol — is an attempt to fix this at the protocol level. It’s open, standardized, and gaining adoption fast enough that it’s worth understanding now.
What MCP actually is
MCP is a client-server protocol that standardizes how AI models communicate with external tools and data sources.
An MCP server exposes capabilities: tools (functions the model can call), resources (data the model can read), and prompts (templates). An MCP client — typically the model runtime or agent framework — connects to servers and makes those capabilities available to the model.
The model never talks to your Slack API, your database, or your GitHub integration directly. It talks to the MCP layer, which handles the rest.
The analogy that makes it click
Think of MCP as USB-C for AI tooling.
Before USB-C, every device had its own connector. You carried five cables. Every new device meant a new compatibility problem.
USB-C didn’t change what devices do — it standardized how they connect. Now one cable works for everything that adopts the standard.
MCP does the same for AI integrations. Before MCP, every agent framework had its own tool-calling convention. Every integration was custom. Every swap meant rewriting glue code.
With MCP: write the server once, use it with any compliant model. Write the agent once, connect to any compliant server. The compatibility surface collapses.
Why it’s gaining traction now
Three things aligned:
Anthropic published the spec and opened it. MCP is not a proprietary Claude feature — it’s an open protocol. Other providers and frameworks adopted it quickly because they had no reason not to.
The timing matched the maturity curve. Tool calling is now stable across major models. The ecosystem needed a transport standard, not a capability one.
Tooling arrived fast. Within months of the spec, there were MCP servers for dozens of popular services. The long tail of integrations is being built by the community, not a single company.
What this means for how you build
If you’re building an AI agent today, the practical implication is this: build your tool integrations as MCP servers, not as custom functions baked into your agent.
The short-term cost is a bit more structure. The long-term payoff is that your integrations become portable — any agent, any model, any runtime that speaks MCP can use them.
The ecosystem is converging here. Building MCP-native now means less migration work later, more reuse, and tools that other builders can benefit from too.
That last part matters if you’re building in public.