MCP is the integration layer that will stick

Before a shared protocol, connecting an AI product to another piece of software meant one of two things. Either the software vendor built a bespoke integration with that AI product, or a developer wrote custom glue code against the software's own API, using whatever authentication and data shapes it happened to expose. Both scale badly. The first means a vendor maintains a separate integration for every AI client anyone wants to use, and that grows with the number of AI products on the market. The second pushes the cost onto every developer who wants to connect the two, repeated every time.

MCP standardizes the description, not the content. A tool exposed over MCP declares its name, what inputs it takes, and what it does, in a format any MCP-compatible client already knows how to read. The vendor writes the tool once. Every MCP client that connects can discover and call it. The vendor writes nothing client-specific, and the developer connecting them writes no custom glue per client either.

This is not a new dynamic. The same pattern has played out with file formats, network protocols and data formats. A closed, vendor-specific approach can be more polished at first, because one company controls the whole experience. But every other party who wants to interoperate has to build a custom bridge to that one vendor, and that cost multiplies with every integration. An open protocol starts slower, because no single company drives all the polish. Each new adopter then makes it more valuable for every existing adopter. The same tool now works with one more client, for free.

Applied to AI integrations: a vendor that only builds a proprietary chat assistant is betting that its own assistant is the one users will want, indefinitely. A vendor that exposes its actions over MCP is betting that being reachable by whichever AI client wins matters more than controlling the interface. Given how fast the set of popular AI clients has already changed, the second bet is the more defensible one for a product that is not itself trying to be the AI client.

Adopting MCP is not only picking a wire format. It forces the same access-control questions any multi-client integration surface would. SalesCrew's MCP server, 145 registered tools served as a Supabase edge function per client instance, is authenticated with per-user tokens. They 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, with the actor recorded as a user, a token, or an agent. None of that comes from the protocol automatically. It has to be designed on top, the same way REST alone includes no access model.

This matters beyond SalesCrew. Any team evaluating a vendor's AI-native claim should ask whether the integration surface is open, MCP or an equivalent standard, or closed, a single proprietary assistant. The open version means the integration outlives any one AI client's popularity. The closed version ties a customer's automation investment to whichever assistant the vendor built first. That is a weaker bet while the AI client landscape keeps shifting under everyone.