Lately, I’ve been deep-diving into the future of AI agent orchestration and came across something that piqued my curiosity: MCP.
I had a vague sense that it was called the “Media Control Protocol” at first, but I quickly realized that I was chasing a different kind of intelligence entirely.
It turns out I was looking for the Model Context Protocol, an open standard developed by Anthropic that is quietly becoming one of the most potent concepts in agent-based AI system design. This blog is a walkthrough of what I learned, built around the questions I asked and the insights I gained during my live voice session with ChatGPT.
What Is MCP (Model Context Protocol)?
MCP is a protocol designed to help coordinate large language models (LLMs) and external systems. In essence, it acts like a central server or orchestrator that allows different agents to:
- Access internal data
- Talk to external APIs
- Work in concert to complete complex tasks
It’s the infrastructure behind the scenes that ensures everything stays organized, secure, and efficient.
Imagine this: You’re building a digital assistant that can read emails, schedule meetings, and summarize customer chats. Instead of writing spaghetti code to pass context and data between these systems, MCP acts as the glue, managing context and control so each LLM or agent knows what to do and when.
Is MCP the Same as an Agent Platform?
Is MCP a platform like LangChain, Wrap.dev, or CrewAI?
The answer: not quite.
While agent platforms help you build workflows that chain multiple tools and LLMs together, MCP is a protocol, a common language, and a structure that allows all those tools to talk to each other seamlessly. You can think of agent platforms as apps and MCP as the standard that makes those apps compatible.
In fact, many agent platforms could use MCP behind the scenes, especially as the ecosystem matures.
What’s the Role of an MCP Server?
Here’s where it gets exciting. An MCP server acts as the central coordinator in this whole setup. Rather than each agent or LLM independently calling APIs and juggling tokens, the MCP server handles:
- Session memory and context tracking
- Secure access to external APIs
- Decision-making about which tool or agent to activate
- Flow control between components
I asked: Is this server real? Is it like a software I can install?
Yes. You can run your own MCP server on a bare-metal machine, a VM, or deploy it as a cloud service. Anthropic’s reference implementation is open source, and others are starting to adopt it in agent frameworks.
Can One MCP Server Serve Multiple Unrelated Apps?
Yes again. Think of the MCP server as a router or middleware hub. You can have multiple applications, one that schedules meetings and another that classifies emails connected to the same MCP instance.
A separate agent or context could represent each app, and the MCP server manages routing, isolation, and authentication.
However, for highly specialized or regulated applications, you may choose to deploy separate MCP servers for each use case.
Can I Use MCP with OpenAI or Other LLMs?
Another key question I asked: Is MCP limited to Anthropic’s Claude models?
The answer: Nope! The protocol is open and model-agnostic.
You can use it with:
- OpenAI (ChatGPT/GPT-4)
- Google’s Gemini
- Meta’s LLaMA
- Open-source models like DeepSeek or Mistral
This is one of the reasons MCP is gaining traction—it’s built for interoperability across the AI ecosystem.
How Is MCP Different from Just Using APIs?
At this point, I wondered: Why not just call APIs myself and pipe results into an LLM?
You can do that, but it becomes messy fast. MCP helps with:
- Standardized interfaces for tools and agents
- Centralized control over context and flow
- Security and monitoring of API calls
- State and memory management across interactions
This makes your system more powerful, maintainable, and scalable. You don’t have to hardcode logic into each component or worry about where data is accessible.
Platforms Like Wrap.dev and LangChain: Where Do They Fit?
Tools like Wrap.dev, LangChain, and CrewAI can be seen as agent platforms that manage orchestration logic, kind of like app builders for AI.
Wrap.dev, for example, is excellent for testing and deploying agent workflows, especially for devs who want visual feedback or want to interactively debug agent behavior.
Some of these platforms may eventually adopt MCP as their underlying standard to simplify integration.
For developers, marketers, and founders like me, building tools powered by LLMs, MCP is the missing piece that enables long-term, scalable AI solutions.
You don’t have to reinvent the wheel with every new tool or agent. Instead, you can build once and plug it into a standardized protocol.
This is exactly the kind of insight we want to explore and share at Kuware.AI—helping agencies and businesses leverage the latest in AI infrastructure to solve real-world problems.
Want Help Implementing MCP?
Whether you are experimenting with AI agents or looking to launch production-grade solutions, we can help. At Kuware.AI, we help agencies and business owners assess, implement, and scale AI technologies without drowning in technical jargon.
Need a strategy session? Book a call, and we’ll help you understand how to use MCP or similar architectures in your business.
