Model Context Protocol

Before a shared standard, every connection between an AI application and an external system was bespoke. Supporting a dozen tools across a few assistants meant writing and maintaining a matrix of custom integrations.

The Model Context Protocol standardizes that connection. A system exposes its capabilities once as an MCP server; any MCP-capable application can then use it without either side knowing about the other in advance. Introduced by Anthropic and released as an open standard, it has since been adopted well beyond its origin — the practical effect is that integration effort scales additively instead of multiplicatively.

Standards decide whether an ecosystem compounds or fragments. MCP means a connector built for one assistant works with others, which lowers the cost of building integrations and reduces the risk of committing to a single vendor's plugin format.

It also concentrates a security question. An MCP server is a granted capability — a channel through which an assistant can read data and take action — so which servers are connected, and with what permissions, is a genuine access-control decision rather than a configuration detail.

That it is an Anthropic-only technology. It originated at Anthropic and was released as an open standard, and adoption now extends well beyond it. Treating it as one vendor's plugin format gets the strategic point backwards: the reason to build on it is precisely that a connector written once works across MCP-capable applications instead of being tied to whoever you built it for.

That it makes a model smarter. It is plumbing, not capability. MCP standardizes how an assistant reaches a tool or a data source; it does nothing for the model's reasoning. The gain is in integration economics — effort that used to scale with applications multiplied by tools now scales additively — which is a large practical win and not a capability one.

That connecting a server is a configuration detail. An MCP server is a granted capability: a channel through which an assistant can read data and take actions in a real system. Which servers are connected, and with what permissions, is an access-control decision deserving the same review you would give any software granted reach into your systems. That one can be added in a few clicks is not evidence that it is low-risk.

If you are building an AI integration today, MCP is the default shape to build it in. If you are connecting third-party MCP servers to an assistant, review them the way you would review any software granted access to your systems.