🎯 Core Goals
- Learn about the industry standard for connecting LLMs to tools.
- Understand that MCP goes beyond “fetching data” — it can supply tools, data, and context.
Before MCP, every AI tool needed custom code to talk to every LLM. MCP standardizes this, letting any AI model connect to any tool, data source, or context provider through one universal interface.
👁️ Visuals & Interactives
MCP: The USB-C of AI
One standard connection for tools, data, and context.
Before MCP: Each AI needed a different "cable" for every tool.
With MCP: One standard connection works for everything — whether you're fetching data, running tools, or loading context.
📝 Key Concepts
- Standardization: Just like USB-C replaced dozens of different charging cables, MCP replaces hundreds of custom integrations. Any AI model, any server — one standard.
- MCP Servers: Small programs that plug into the AI. They can expose three very different things:
- Tools — actions the AI can trigger (e.g., run a web search, execute code, send an email)
- Data — files, databases, GitHub repos the AI can read from
- Context — pre-loaded knowledge like best practices guides, travel planning templates, or domain glossaries
- MCP Clients: The AI models (like Claude) that “plug into” those servers.
- AI-native interfaces: Many software tools now ship with built-in MCP support — command-line tools, project managers, and CRMs increasingly offer an AI-ready interface so agents can operate them directly.
MCP isn’t just for pulling in data. A company could create an MCP server that loads their internal expense policy, coding standards, or customer service playbook — giving the AI instant context without you having to paste it into every prompt.
The MCP ecosystem is evolving fast. It’s worth searching for “best MCP servers 2025” or “what tools does Claude use” to see what’s available today — from web browsers and spreadsheet editors to code runners and calendar integrations. What you find might surprise you.
What problem does MCP (Model Context Protocol) solve?