MCP: The Emerging Force in AI Communication
We all know about model context protocol (MCP) in AI. This article explores its significance and potential. With APIs around, why MCP? Despite MCP’s growing popularity, will it last? First, we compare APIs and MCP, then delve into MCP’s unique aspects.
From APIs to MCP
A standalone computer has limited data access. APIs facilitate data exchange between systems. Similarly, Model Context Protocol (MCP) enables AI agents using large language models (LLMs) to communicate. APIs target developers, while MCP is for AI agents (Johnson, 2025).
Understanding MCP
Released by Anthropic on November 25, 2024, as an open source standard, MCP facilitates AI assistants’ interaction with external data. AI agents face data fragmentation issues in isolated systems (Anthropic, 2024). The protocol guides agent interactions with external systems, user prompts, and automation.
MCP employs the client-server model, with three key features for each.
MCP servers: tools, resources, prompts MCP clients: elicitation, roots, sampling This article focuses on the main client and server features. MCP servers use tools for complex tasks, while clients use elicitation for two-way communication between agent and user. Agents choose tools based on user input. If a tool requires parameters, agents use elicitation for user data, enabling responsive communication between LLM and user.
Why MCP Now?
Why MCP when APIs enable two-way communication in fragmented data systems and SaaS apps? The user has shifted from developers to AI agents. Developers program deterministic apps via APIs, whereas AI agents use prompts to autonomously fulfill user requests. AI agent workflows are inherently non-deterministic.
APIs offer a deterministic machine-executable contract. They work when users know subsequent actions (Posta, 2025). AI agents rely on probabilistic LLMs, which don’t ensure repeatable results for all tasks (Atil, 2024), causing issues in automation.
MCP’s Solution
MCP tackles agent execution variance with high-level abstractions over functionality, not just API endpoints. Tools empower LLMs for actions like flight search and calendar booking (Understanding MCP Servers, 2026).
Tools aren’t API call abstractions. Instead, they’re functionality abstractions. Numerous APIs exposed as tools would increase agent costs and context size, which isn’t ideal (Johnson, 2025). Tools may include multiple APIs for desired outcomes. Agents assess tool availability and order of execution.
MCP’s Growing Adoption
Since 2024, MCP’s popularity has surged. A Google Trends chart depicts MCP’s rising interest since launch. By February 2026, the official MCP registry had over 6400 servers. The registry is in preview, and the ecosystem continues to expand rapidly.
Prominent companies have adopted MCP, launching their servers for autonomous agents. OpenAI added ChatGPT MCP support in March, with Google following in April 2025, indicating the protocol’s enduring presence and swift adoption.
What Awaits?
MCP is in its early adoption phase, with many applications still maturing. Leonardo Pineryo from Pento AI aptly summarized, “MCP’s first year transformed how AI systems connect to the world. Its second year will transform what they can accomplish” (2025).
Enhancing tool guardrails is crucial to address trust concerns with AI agents. Improved guardrails will enable greater autonomy. MCP is poised for continued growth, both in capability and application volume.