MCP (Model Context Protocol): The USB Standard for AI Tools

Before USB, every peripheral shipped with its own connector and driver. Before MCP, every AI integration was the same story: we'd write a connector for our agent, then rewrite the same logic when a client wanted it in Claude Desktop, then again for their IDE assistant. The Model Context Protocol — open-sourced by Anthropic and since adopted across the major AI clients — is the standard plug: build the integration once as an MCP server, and any MCP-capable client can use it.

The Shape of the Protocol

MCP is a client-server protocol over JSON-RPC. A server wraps a system (your database, your CRM, your file store) and exposes three kinds of things:

The client (Claude Desktop, an IDE, your own agent runtime) discovers what a server offers at connect time and mediates the model's calls to it. Transport is stdio for local servers or HTTP for remote ones. That's essentially the whole standard — and the smallness is the point.

What Changes Economically

The old math: M clients × N systems = M×N bespoke integrations. The MCP math: N servers, M clients, done. For an agency like ours this is concrete money. A connector we build for one client's custom agent used to be locked inside that agent. As an MCP server, the same connector plugs into Claude Desktop for their ops team, into Cursor for their developers, and into our own agent runtime — three surfaces from one codebase.

The integration stops being a feature of your app and becomes an asset of your organization.

What MCP Does Not Solve

Worth being blunt here, because the hype skips it:

• Auth is your problem. The spec supports OAuth for remote servers, but mapping "which user is asking" to "what rows can they see" is still your code. A server with a service-level credential exposes everything that credential can reach — to every connected client.
• Tool quality is your problem. A model with 40 overlapping, vaguely-described tools performs worse than with 8 sharp ones. MCP standardizes the plug, not the ergonomics. Descriptions, parameter constraints, and result formatting still decide whether the model uses your server well.
• Trust is your problem. An MCP server is arbitrary code that receives model-generated inputs, and tool results feed straight back into the model's context — prompt injection through tool output is real. Treat third-party servers like npm dependencies with shell access: pin, review, sandbox.

How We Build Servers

1. Wrap the service layer, not the database. Our MCP servers call the same internal APIs our backend uses — permission checks and business rules come along for free.
2. Few tools, strongly typed. We aim for under 10 tools per server, each with tight JSON Schema and a one-sentence description written for the model, not for documentation.
3. Return structured, compact results. Tool output is model context; a 200KB JSON dump burns tokens and drowns the answer. Summarize, paginate, include IDs for follow-up calls.
4. Log every call. Tool-call logs are simultaneously your audit trail, your debugging tool, and your eval dataset.

Should You Adopt It Now?

If you're building integrations for AI systems in 2026: yes, MCP-first is the sane default. The ecosystem momentum is one-directional, and the cost of wrapping an existing internal API as an MCP server is typically a day or two. The exception is deeply latency-sensitive in-process tool use inside a single agent, where a direct function call still beats a protocol hop. For everything else, build the plug once and stop rewriting drivers.