API vs MCP

API vs MCP

by | Mar 6, 2026 | Uncategorized @sq

MCP vs API — Simply Explained
Simply Explained

API vs MCP

Two ways software talks to other software — and why one is simpler than the other.

Analogy — Restaurant
Imagine you’re at a restaurant. An API is like ordering directly from the kitchen — you must know the exact menu, use the exact dish names, and speak the kitchen’s language. Every restaurant has its own unique menu you must learn.

MCP is like having a universal waiter who knows every restaurant’s language. You just say “I’d like something spicy and vegetarian” — and the waiter handles all the kitchen-specific details for you, no matter which restaurant you’re at.
Analogy — Phone Call
An API is like calling a specific company’s support line — you need their exact number, you follow their exact phone menu, and you speak their specific process. Every company has a different system to learn.

MCP is like having a universal translator assistant who makes calls on your behalf. You tell your assistant what you need in plain terms, and they know how to navigate every company’s phone system for you.
Analogy — USB Port
Traditional APIs are like old-fashioned connectors — every device had its own proprietary plug. You needed a different cable for every device, and they weren’t interchangeable.

MCP is like USB-C — one universal standard connector that works everywhere. Plug any device into any port and it just works, without needing to know the device’s internal specifics.
The Classic
API
Application Programming Interface. A set of rules that lets two pieces of software talk to each other — on the software’s terms.
  • Each service has its own unique API to learn
  • You send a specific request, get a specific response
  • Like a custom door with a custom key
  • Requires you to know exactly what to ask for
  • Been around for decades — extremely common
The New Standard
MCP
Model Context Protocol. A universal standard that lets AI models connect to any tool or service using one common language.
  • One protocol that works across all services
  • Built specifically for AI to use tools intelligently
  • Like a universal key that opens any door
  • The AI figures out what to ask — you just give the goal
  • Brand new (2024) — rapidly being adopted
How the connections look
API
Your App
──API 1──▶
Google Maps
Your App
──API 2──▶
Slack
Your App
──API 3──▶
Stripe
Every connection is custom — you learn each one separately.
MCP
AI Model
── MCP ──▶
MCP Server
──▶
Any Tool
One universal protocol. The AI talks to an MCP server, which handles all the tool-specific details.
💡
APIs aren’t going away — MCP is built on top of them.
MCP doesn’t replace APIs. Under the hood, MCP servers still use APIs to talk to services.
MCP just adds a standard layer on top so AI models don’t have to learn every API individually.
When would you use each?
Use an API when…
Building a specific integration for your app
You need precise control over every request
Connecting two non-AI systems to each other
The service doesn’t have an MCP server yet
You’re a developer writing direct integrations
Use MCP when…
Connecting an AI model to external tools
You want the AI to use many tools at once
Building AI assistants or agents
You want a plug-and-play setup for AI tools
Enabling AI to take real-world actions for you
Fine-tuning vs RAG — Simply Explained

Fine-tuning vs RAG — Simply Explained

by | Mar 6, 2026 | Uncategorized @sq

Fine-tuning vs RAG — Simply Explained
Simply Explained

Fine-tuning vs RAG

Two ways to make an AI smarter about your specific topic — and when to use each one.

Analogy — Student
Imagine training a student to be a medical expert. Fine-tuning is like putting them through medical school — months of intensive study until the knowledge becomes second nature. They don’t need to look anything up; it’s all in their head. But updating what they know means going back to school.

RAG is like giving that same student a medical library they can search in real time. They look up the latest research before answering. Their answers are always fresh and up-to-date — but they need the library nearby.
Analogy — Chef
Fine-tuning is like training a chef to memorize your restaurant’s entire menu — every recipe, every technique, baked into muscle memory. Fast, fluent, no hesitation. But if the menu changes, they need retraining.

RAG is like giving a chef a constantly updated recipe book they can flip through before cooking. The menu can change every day and they’ll always make the right dish — as long as the book is accurate and at hand.
Analogy — New Employee
Fine-tuning is like an intensive onboarding program — weeks of training until the employee deeply understands your company’s culture, tone, and processes. They just know how things work here.

RAG is like giving a smart new hire access to the company wiki and Notion docs. They look things up as needed. Add a new policy today and they’ll know it tomorrow — no retraining required.
Teach the model
Fine-tuning
You retrain an existing AI model on your own data, so the new knowledge becomes baked directly into its weights — part of who it is.
  • Knowledge is internalized, not looked up
  • Faster responses — no retrieval step needed
  • Great for style, tone, and format changes
  • Expensive and slow to update
  • Can “forget” or hallucinate outdated info
Give the model a library
RAG
Retrieval-Augmented Generation. The AI searches a knowledge base at query time and uses what it finds to answer — like open-book vs closed-book.
  • Knowledge stays fresh — update docs, not the model
  • Answers are grounded in real, citable sources
  • Great for large, changing knowledge bases
  • Slightly slower due to retrieval step
  • Only as good as the documents it can find
How each approach works
Fine-
tuning
Base Model
+ your data ──▶
Trained Model
──▶
Answer
Your data is baked in permanently. Fast at inference, but costly to update.
RAG
Base Model
◀── fetches ──
Your Docs
Model + context
──▶
Answer
Docs stay separate. Model retrieves relevant chunks at query time — always up to date.
How they compare
High cost Training cost Low cost
Slow to update Knowledge freshness Always fresh
Strong Tone & style control Weaker
Harder Ease of setup Easier
Can hallucinate Factual reliability Cites sources
◀ Fine-tuning wins here RAG wins here ▶
💡
Most production AI systems use both together.
Fine-tune the model to speak in your brand’s voice and format.
Use RAG to give it access to your latest documents and data.
They’re complementary, not competing.
When would you use each?
Use Fine-tuning when…
You need a specific tone, style, or persona
Your data is stable and doesn’t change often
You want the model to follow a strict format
Speed matters and you can’t afford retrieval latency
Teaching skills, not facts (e.g. “write like us”)
Use RAG when…
Your knowledge base changes frequently
You need answers grounded in specific documents
You want the AI to cite its sources
You have a large library of internal docs or PDFs
Building a Q&A bot over your own content