Model Context Protocol (MCP): Foundation for AI or a Looming Risk? — AI Innovations and Insights 37

Author(s): Florian June

Originally published on Towards AI.

Welcome to the 37th installment of this elegant series.

Model Context Protocol (MCP) was introduced by Anthropic in 2024 to tackle the complexity and scalability challenges of manually connecting AI applications to external APIs. It has been quite a hot topic lately.

I wrote an introductory article about it a while back (MCP = HTTP or USB-C for the AI World?), but recently I discovered a new survey (Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions) — and from what I’ve seen, it appears to be the first serious, comprehensive academic overview of MCP. I thought it would be valuable to share it with you all.

Before MCP came along, AI applications relied on various methods — manual API wiring, plugin-based interfaces, agent frameworks — just to connect with external tools.

Figure 1: Tool invocation with and without MCP. [Source].

As shown in Figure 1 (left), every external service meant dealing with a specific API, adding complexity and making it harder to scale.

As shown in Figure 1 (right), MCP changes that by introducing a standard protocol that streamlines how AI models connect with external tools — standardizing the way they retrieve data and perform actions.

The MCP architecture has three main components: MCP… Read the full blog for free on Medium.

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Published via Towards AI