Why MCPs Will Change How You Build with AI (Forever)
Everyone is talking about MCPs, but most people don't understand what they are or why they matter. Fortunately, Ras Mic came on the podcast to shed some light. In this article, we'll break down exactly what Model Context Protocols are, why they're transforming AI development, and what opportunities they create for businesses and developers.
What You'll Learn
What MCPs are and why they're gaining popularity
How MCPs differ from traditional LLM tools integration
The technical architecture behind MCPs
Why MCPs are the next evolution in AI development
Potential business opportunities in the MCP ecosystem
Understanding MCPs: The Problem They Solve
Before diving into MCPs, let's understand a fundamental limitation of Large Language Models (LLMs): by themselves, LLMs can't do anything meaningful beyond generating text.
If you ask ChatGPT to send an email or analyze data from your spreadsheet, it simply can't do it. LLMs are primarily designed to predict the next word in a sequence. When you type "My Big Fat Greek," the model predicts "Wedding" as the next word based on its training data.
The Evolution of LLM Capabilities
AI development has gone through distinct phases:
Basic LLMs: Generate text responses to prompts
LLMs + Tools: Connect external services to extend capabilities
MCPs: Standardize how LLMs interact with external tools
The second phase—connecting tools to LLMs—created applications like Perplexity, which can search the internet. However, building these integrations is complex, requiring custom engineering for each new service or capability.
What Exactly Are MCPs?
MCP stands for Model Context Protocol. Think of it as a standardized layer between your LLM and external services.
If each external tool speaks a different "language" (with its own API requirements), MCPs act as a universal translator. They convert these different formats into a unified structure that language models can understand and use.
In essence, MCPs provide a standard protocol for LLMs, defining how services should expose their capabilities to be accessible in a predictable way. This standardization makes it far easier to connect LLMs to outside resources—databases, search engines, email services, and more.
The MCP Ecosystem Architecture
The MCP ecosystem consists of four key components:
MCP Client: The LLM-facing side (examples include Tempo, Wind Surf, and Cursor)
MCP Protocol: The standardized communication method between client and server
MCP Server: Translates between the protocol and external services
Service: The actual external capability (database, search engine, etc.)
What makes this architecture particularly clever is that the MCP server responsibility falls to service providers. If you run a database company and want LLMs to easily access your service, you build an MCP server following the protocol standards.
Why MCPs Matter: The Benefit to Developers
MCPs solve one of the biggest challenges in AI development today: AI tool integration.
Traditionally, if you wanted to build a multi-function AI assistant, you had to:
Find external services for each capability
Connect each one to your LLM
Ensure the LLM uses them correctly
Make all these tools work together cohesively
This approach is fragile—one small change can crash the whole setup.
MCPs solve this by standardizing the connection process.
The difference is substantial:
Without MCPs: Building an AI assistant that can search the web, read emails, and update spreadsheets requires custom engineering for each capability.
With MCPs: These capabilities can be plugged in more easily through a standard protocol.
This unlocks the potential for more advanced AI agents—assistants that don’t just generate responses, but actually perform real-world tasks across services.
Technical Challenges and Current State
MCPs aren't perfect yet. Setting up an MCP server currently involves many manual steps and workarounds. But as the standard matures, these initial challenges will likely be resolved.
The industry is still early in adopting MCPs. Companies like Anthropic are essentially creating a standard they hope everyone will follow, similar to how web standards evolved.
Business Opportunities in the MCP Ecosystem
For technical users, there are immediate opportunities:
MCP App Store: Creating a marketplace where users can easily find and install MCP servers
MCP Server Development: Building servers for popular services
MCP Integration Tools: Creating tools that simplify the process of working with MCPs
For non-technical users and businesses, the best approach is to:
Stay informed about platforms building MCP capabilities
Monitor how standards evolve
Be ready to leverage standardized AI connections when they mature
The Future of AI Development with MCPs
MCPs are a major step toward building AI agents that don't just talk - they act. By standardizing AI tool integration, they remove one of the biggest friction points in AI app development.
As adoption grows, we’re likely to see an explosion of specialized agents, new startups, and even AI-powered “operating systems” for businesses.
While we're still in the early stages, MCPs are poised to fundamentally change how we build with AI, potentially enabling the kind of seamless AI assistants we've only seen in science fiction.
Conclusion
MCPs may sound technical, but their impact is straightforward: they make it dramatically easier to build useful AI applications by standardizing how LLMs connect to external services and tools.
We’re still early in this shift, but it’s already reshaping how developers approach AI tool integration. Whether you're technical or non-technical, understanding MCPs now will give you a serious advantage when the ecosystem matures.