MCP is the integration layer that will stick

Before a shared protocol, linking an AI product to other software meant one of two things. Either the software vendor built a custom integration with that AI product, or a developer wrote glue code against the software's own API. That code had to use whatever login method and data shapes the API happened to have. Both paths scale badly.

Path one means a vendor keeps a separate integration for every AI client people want to use. That list grows with every new AI product on the market. Path two pushes the cost onto every developer who wants to connect the two, and they pay it again each time.

MCP sets a standard for the description, not the content. A tool exposed over MCP states its name, the inputs it takes and what it does. It uses a format any MCP client already knows how to read. A vendor writes the tool once, and every MCP client that connects can find it and call it. It writes nothing per client, and neither does the developer connecting them.

You've seen this pattern before, with file formats, network protocols and data formats. A closed, vendor-owned approach can feel more polished at first, because one company controls the whole experience. But everyone else who wants to connect has to build a custom bridge to that one vendor. That cost multiplies with every integration.

An open protocol starts slower, since no single company drives all the polish. Then each new adopter makes it worth more to every existing one. The same tool now works with one more client, for free.

Now apply that to AI. A vendor that only builds its own chat assistant is betting users will want that assistant forever. A vendor that exposes its actions over MCP is making a different bet. It says being reachable by whichever AI client wins matters more than owning the screen. Popular AI clients have already changed fast. So the second bet is the safer one for any product that isn't trying to be the AI client itself.

Adopting MCP isn't only picking a wire format. It raises the same access questions as any surface that many clients connect to. SalesCrew's MCP server has 145 registered tools. It runs as a Supabase edge function in each client instance, and it checks a token for each user. Those tokens are scoped by the same three axes that govern the UI: channel, Upwork profile and area.

Every tool call is written to an audit log. It records the actor as a user, a token or an agent. None of that comes free with the protocol. It has to be designed on top, just as REST alone has no access model.

This matters beyond SalesCrew. When you judge a vendor's AI-native claim, ask whether its integration surface is open or closed. Open means MCP or a similar standard. Closed means a single in-house assistant. With the open version, your integration outlives any one AI client's run of popularity. A closed one ties your automation work to whichever assistant the vendor built first. That's a weaker bet while the field of AI clients keeps shifting under everyone.