Exploring Opportunities with MCPs: Insights for Builders

I’m joined by Ras Mic to explain MCPs. Mic breaks down how MCPs essentially standardize  how LLMs connect with external tools and services. While LLMs alone can only predict text, connecting them to tools makes them more capable, but this integration has been cumbersome. MCPs create a unified layer that translates between LLMs and services, making it easier to build more powerful AI assistants.

Timestamps:
00:00 - Intro
02:39 - The Evolution of LLMs: From Text Prediction to Tool Use
07:51 - MCPs explained
11:11 - MCP Ecosystem Overview
13:59 - Technical Challenges of MCP
15:18 - Conclusion on MCP's Potential
16:00 - Startup Ideas for Developers and Non-Technical Users

Key Points:

• MCP (Model Context Protocol) is a standard that creates a unified layer between LLMs and external services/tools
• LLMs by themselves are limited to text prediction and cannot perform meaningful tasks without tools
• MCP solves the problem of connecting multiple tools to LLMs by creating a standardized communication protocol
• The MCP ecosystem consists of clients (like Tempo, Windsurf, Cursor), the protocol, servers, and services

1) What are MCPs and why should you care?

MCPs are NOT some complex physics theory - they're simply STANDARDS that help LLMs connect to external tools and services.

Think of them as universal translators between AI models and the tools they need to be truly useful.

This is HUGE for making AI assistants actually capable!

2) The Evolution of LLMs: From Text Prediction to Tool Use

Stage 1: Basic LLMs can only predict text
• Ask ChatGPT to send an email? "Sorry, I can't do that"
• They're glorified text predictors (if I say "My big fat Greek..." it knows "wedding" comes next)
• Limited to answering questions, not DOING things

3) The Current State: LLMs + Tools

Stage 2: LLMs connected to tools
• Companies like Perplexity connect LLMs to search engines
• This makes them more useful but creates problems
• Each tool = different "language" the LLM must learn
• Connecting multiple tools = engineering NIGHTMARE

This is why we don't have Jarvis-level assistants yet! 

4) Enter MCPs: The Game-Changer

MCPs create a UNIFIED LAYER between LLMs and external services.

Instead of your AI speaking 10 different "languages" to use 10 different tools, MCPs translate everything into ONE language.

Result? LLMs can easily access databases, APIs, and services without massive engineering headaches.

5) The MCP Ecosystem Explained

The MCP system has 4 key components:

• MCP Client: User-facing apps like @tempoai, Windsurf, Cursor
• Protocol: The standardized communication method
• MCP Server: Translates between client and services
• Service: The actual tool (database, search engine, etc.)

Brilliant move by Anthropic: SERVICES must build MCP servers!

6) Why This Matters For Builders

For technical folks:
• Opportunity to build tools like MCP app stores
• Easier integration between services
• Less engineering headaches

For non-technical folks:
• Watch closely as standards evolve
• When standards finalize, new business opportunities will emerge
• Think of MCPs as Lego pieces you'll stack to build powerful AI apps

Notable Quotes:

"LLMs by themselves are incapable of doing anything meaningful... The only thing an LLM in its current state is good at is predicting the next text." - Ross Mike

"Think of every tool that I have to connect to make my LLM valuable as a different language... MCP, you can consider it to be a layer between your LLM and the services and the tools." - Ross Mike

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