MCP vs Agents: Understanding the Technologies Shaping AI's Future

How OpenAI's adoption of the Model Context Protocol signals a new era of interoperability

After publishing my article on the Model Context Protocol (MCP) and the Rise of AI Agents, I've had numerous conversations with readers who remain confused about where agents end and MCP begins, when to use one versus the other, and whether they compete or complement each other.

The confusion is understandable. Both technologies emerged rapidly in the AI ecosystem, and their boundaries can seem blurry. With OpenAI's recent announcement that they've added MCP support to their Agent SDK and will soon roll it out to the ChatGPT desktop app and Responses API, this distinction becomes even more important to understand.

The MCP Ecosystem Expands

OpenAI's adoption of MCP represents a significant milestone. When Sam Altman tweeted, "people love MCP and we are excited to add support across our products," it marked a clear acknowledgment that the industry is rallying behind Anthropic's open protocol. With ChatGPT now getting behind MCP, alongside other popular applications like Cursor, we're seeing major AI interfaces embracing this standardized approach to extensibility.

This momentum positions MCP to become the universal protocol for AI applications to access external data and functionality, much like how USB-C has become the standard connector for various devices.

Defining the Technologies

What Are AI Agents?

AI agents are goal-seeking, autonomous applications that leverage AI to observe and respond to external events and signals. They:

1. Listen for external triggers or inputs (which could simply be time passing)

2. Reason about and interpret these signals

3. Formulate action plans in response

4. Take autonomous actions to achieve their objectives

5. Observe and assess the impact of their actions, and iterate (rinse and repeat until satisfied with the outcome)

For example, a code review agent might monitor pull requests in GitHub, automatically review code, find issues, suggest improvements, and even issue new pull requests with fixes - all working toward the goal of improving code quality.

What Is MCP?

The Model Context Protocol is an open protocol for extending AI applications. It's most helpful to think of MCP as a plugin system for AI applications - similar to Chrome extensions or VS Code plugins. It categorizes functionality into three main types:

1. Tools: Functions that take actions or invoke services

2. Resources: Gatherers of context from databases, file systems, etc.

3. Prompts: Templated instructions that help structure AI responses

MCP represents an evolution of the function calling capability but within a standardized framework that allows for widespread adoption and compatibility.

Complementary, Not Competitive

These technologies serve different purposes but work together beautifully:

1. Agents can be MCP clients: AI agents can leverage MCP to find and register capabilities they can use when solving their objectives.

2. Agents can be MCP servers: Agents can expose their own specialized capabilities through MCP, allowing desktop applications like Claude or ChatGPT to incorporate them to satisfy user requests.

MCP provides a universal protocol that enables AI applications and agents to communicate with each other in a standardized way, regardless of who created them or what underlying model they use.

The Plugin Architecture for AI

MCP fulfills the role of a plugin architecture for AI applications. Just as browser extensions transformed web browsers from simple page renderers into application platforms, MCP transforms AI interfaces from chat windows into extensible computing platforms.

Within a year, we'll likely see:

1. Almost every major SaaS app supporting MCP for integration with AI systems

2. A growing collection of open-source, self-hosted MCP servers for local capabilities

3. Centralized discovery hubs and registries for MCP servers (think: app stores for AI plugins)

4. Standardized interfaces for common tasks like file system access, database integration, and API connectivity

Human vs. Machine Interfaces

There's another fascinating aspect to consider: MCP represents a shift in interface design. While traditional UIs are optimized for human cognitive limitations (chunking information, providing visual feedback, etc.), MCP creates machine-optimized interfaces.

An AI agent doesn't need the same UX affordances as humans. It can process complex schemas with thousands of fields in a single operation. As agents become primary consumers of our systems, we'll need to design interfaces explicitly for machine consumption, with human interfaces serving as alternative paths rather than primary ones.

Looking Forward

The industry recognition of MCP, evidenced by OpenAI's adoption, signals a maturation in how we think about AI system design. Rather than each provider creating proprietary extension methods, a standardized approach allows for greater interoperability and innovation.

For developers and businesses building AI applications, this means:

1. Having a consistent, well-documented way to extend AI systems

2. Being able to leverage tools across different AI frameworks

3. Building once and deploying across multiple AI platforms

As we move forward, expect to see deeper categorization within MCP for specialized use cases and more sophisticated patterns of agent-to-agent communication mediated through this protocol.

Conclusion: The Universal Connector

MCP empowers agents. Think of MCP as the universal connector that allows different AI systems to communicate and share capabilities, while agents are the autonomous workers leveraging those connections to achieve specific goals.

The agent ecosystem and the MCP protocol will evolve together, creating a more connected, capable, and interoperable AI landscape. The future belongs to those who understand how to leverage both technologies in tandem.

If you're building applications with AI interfaces and want to offer the ability to make them extensible, look to MCP. And if you're developing autonomous systems that need to react to events and take actions toward objectives, build agents that speak MCP.

For existing service providers and technology companies, creating an MCP server enables AI tools to access your resources and capabilities directly. This integration positions your platform as part of the emerging AI ecosystem without requiring custom implementations for each AI system.

Together, these complementary technologies will shape the next generation of AI applications.