MCP shouldn't be thought of as just another developer API like REST. Its true purpose is to enable seamless, consumer-focused pluggability. In a successful future, a user's mom wouldn't know what MCP is; her AI application would just connect to the right services automatically to get tasks done.

OpenAI integrated the Model-Centric Protocol (MCP) into its agentic APIs instead of building its own. The decision was driven by Anthropic treating MCP as a truly open standard, complete with a cross-company steering committee, which fostered trust and made adoption easy and pragmatic.

Placing MCP within a neutral foundation like the AAIF is a strategic move to build industry confidence. It guarantees the protocol will remain open and not be controlled or made proprietary by a single company (like Anthropic). This neutrality is critical for encouraging widespread, long-term investment and adoption.

MCP was born from the need for a central dev team to scale its impact. By creating a protocol, they empowered individual teams at Anthropic to build and deploy their own MCP servers without being a bottleneck. This decentralized model is so successful the core team doesn't know about 90% of internal servers.

The MCP protocol made the client's return stream optional to simplify implementation. However, this backfired as most clients didn't build it, rendering server-side features like elicitations and sampling unavailable because the communication channel didn't exist. This is a key lesson in protocol design.

A major unsolved problem for MCP server providers is the lack of a feedback mechanism. When an AI agent uses a tool, the provider often doesn't know if the outcome was successful for the end-user. This "black box" makes iterating and improving the tools nearly impossible.

Exposing your platform via a Model Consumable Platform (MCP) does more than enable integrations. It acts as a research tool. By observing where developers and LLMs succeed or fail when calling your API, you can discover emergent use cases and find inspiration for new, polished AI-native product features.

Real-world adoption in specific verticals like finance is shaping the MCP protocol. For example, legal contracts requiring mandatory attribution of third-party data are leading to a "financial services interest group" to define extensions. This shows how general-purpose protocols must adapt to niche industry compliance needs.

According to CTO Malte Ubl, Vercel's core principle is rigorous dogfooding. Unlike "ivory tower" framework builders, Vercel ensures its abstractions are practical and robust by first building its own products (like V0) with them, creating a constant, reality-grounded feedback loop.