MCP has promise beyond AI, and could serve as a “universal plugin system” that connects disparate data sources and tools together to enable new capabilities
Or: The Day My Toaster Started Taking Phone Calls — There's this thing about USB-C that nobody really talks about.
Context & Ripple Effects
MCP began as an open-source way to connect AI assistants with data sources, following Anthropic’s initial standard release. Subsequent coverage focused on its role in agent and search workflows, including Microsoft’s discussion of MCP alongside AI agents.
This article extends that arc: MCP could become a general interoperability layer rather than merely an LLM connector. Its USB-C comparison also emphasizes that a nominally universal interface can still fragment through implementation complexity and uneven compatibility.
First-order effects
- Builders can evaluate MCP as a common interface for linking tools and data sources beyond a single AI assistant, broadening the kinds of integrations it might support.
- High token use remains an immediate constraint for AI-facing implementations, making the protocol’s convenience trade off against context efficiency.
Second-order effects
- Tool vendors and middleware providers may face pressure to expose MCP-compatible interfaces if customers want integrations to travel more easily across assistants and applications.
- The USB-C analogy highlights a likely integration burden: incompatible extensions or partial implementations could shift complexity from point-to-point connectors into testing, compatibility, and support work.
Third-order effects
- If adoption broadens while implementations remain interoperable, MCP could help move integration competition from proprietary connectors toward shared context and tool-access layers.
- If token overhead and compatibility divergence persist, the protocol may instead reproduce the fragmented-plugin problem it aims to solve, with interoperability depending on middleware rather than the standard alone.
The trend: MCP is part of the push to make context and tool access portable across AI agents and, potentially, wider software ecosystems.