Have you ever chatted with an AI that seemed smart but… kind of useless? 😕

Maybe it answered your question but couldn’t remember what you said earlier. Or you asked it to “check the weather,” and it just guessed instead of giving real-time data. It felt like talking to someone who knows everything but can’t do anything.

That’s the frustrating reality of many Large Language Models (LLMs) in today’s apps. While they’re brilliant at understanding and generating text, they often lack memory, context awareness, and the ability to take real action.

And in a world that needs smart assistants, not just smart answers, that’s a big problem.

🎯 That’s exactly where Model Context Protocol (MCP) comes in. It bridges the gap between powerful language models and practical, reliable app behavior—helping LLMs remember, reason, and actually do things.

🧠 What is an LLM (Large Language Model)?

LLMs, or Large Language Models, are advanced AI systems trained on massive amounts of text—books, websites, articles, and more.

Their job?
🧩 To understand the patterns of language and generate human-like responses to the text we give them.

They don’t “think” like humans, but they’re really good at:

✍️ Writing emails, stories, and code
💬 Chatting naturally..etc

Some popular examples include:

ChatGPT by OpenAI
Claude by Anthropic
Gemini by Google

🦜 Analogy Time:

LLMs are like extremely well-read parrots. They've read billions of pages, and while they don’t truly “understand,” they’re amazing at mimicking smart, meaningful conversations.

❌ Limitations of LLMs in Apps

While LLMs are powerful at generating text, they have serious limitations when used inside real-world apps. Here’s where they fall short:

🚫 Common Issues:

🧠 No memory of previous messages or steps unless manually passed
🌐 No access to real-time data like weather, stock prices, or news
🔌 Can’t use tools or APIs to fetch, calculate, or trigger actions
🎭 Hard to control their behavior, role, or tone
🤯 Sometimes they give inconsistent or made-up (hallucinated) answers

🛒 Real-Life Example:

Imagine building a shopping assistant using an LLM.

You: “Suggest a good phone under ₹20,000.”
AI: “Try the Redmi Note 12—it’s affordable and great!”
You: “Is it in stock on Flipkart?”
AI: “Probably…” 😅

💥 Problem: The LLM can’t check live stock or fetch real-time pricing. It’s guessing—because it has no connection to external data or tools.

Without structure or control, LLMs are like very smart people stuck in a library—they can tell you what they read, but they can't step outside to act.

🧪 Solutions That Emerged Before MCP

To overcome the limitations of LLMs (like not being able to fetch real-time data or perform tasks), developers began adding function calling support to models.

⚙️ What is Function Calling?

Function calling lets an LLM use specific tools or actions by calling a predefined function.

For example:

🌀 Want to check the weather? → Add a getWeather() function
📂 Want to open a file? → Add a readFile() function
🔢 Want to calculate something? → Add a calculate() function

This gave LLMs a way to "do" things instead of just "talking."

🔍 But Here’s the Catch:

❗ These early solutions came with their own set of problems:

🧶 1. Messy Code

Every tool had to be manually wired to the LLM
Resulted in a tangled mess of connections

📏 2. Hard to Scale

Adding or updating tools meant rewriting code
Not suitable for apps that needed to grow fast

🔓 3. Security Risks

Exposing multiple tools directly to the LLM could allow unsafe or unintended actions

🔧 4. Difficult to Maintain

If a tool or API changed, every connection to the LLM needed to be reviewed and updated

🛠️ In Short:

While function calling helped LLMs do more, it created a new problem: a fragile, complex, and insecure setup.

That’s where MCP (Model Context Protocol) enters the picture—as a smarter, cleaner solution.

🚀 What is MCP (Model Context Protocol)?

After all the messy workarounds and limitations, MCP comes in as a clean, structured solution.

✅ Clear Definition:

Model Context Protocol (MCP) is a protocol that defines how apps should communicate with LLMs—not just to chat, but to think, remember, act, and solve real tasks step by step.

With MCP, LLMs can now:

📌 Understand the overall goal of the user
🧠 Remember past steps and actions
🔧 Access tools (like weather APIs, file readers, calculators)
🪜 Follow structured workflows, one step at a time

It transforms LLMs from text responders into real action-takers that can fit seamlessly into smart apps.

⚙️ How MCP Works?

🔄 Step-by-Step Workflow

🧠 User types a query in the app
For example: “Find available jobs for summer in New York.”
🗃️ LLM receives the query
The LLM doesn’t know how to fetch job data itself, but it knows what kind of tool it needs.
🪄 LLM chooses a tool
Based on the prompt, it selects something like JobSearchTool.
📦 MCP host sends tool name + parameters to MCP server
It packages the request and asks the MCP server to handle the rest.
🔁 MCP server runs the tool
The tool is executed with real-time data sources (like APIs).
📬 MCP server sends the result back to the host
It returns structured output like job listings, company names, locations, etc.
📤 LLM receives the result and generates a final response
It presents this in natural language, like:

“Here are 5 summer jobs in New York, including a Barista role at Starbucks.”

🌟 Advantages of MCP

MCP doesn’t just improve how LLMs behave—it transforms them into powerful, real-world agents that can be trusted to complete tasks step by step.

Here’s what makes MCP a game-changer:

🎯 1. Goal-Focused Interactions

LLMs no longer just answer one-off questions—they now understand and work toward a defined goal, just like a helpful assistant with a purpose.

🔁 2. Consistent Behavior Across Steps

Forget the randomness. MCP ensures that the LLM follows a structured plan, keeping its responses relevant, accurate, and in sequence.

🧱 3. Modular & Easy to Debug

Since MCP breaks tasks into clear steps and roles, it’s much easier for developers to:

Spot issues
Improve specific parts
Add or remove tools without breaking everything

🌐 4. Enables Real-World Action

Want to fetch live weather, trigger a calendar event, or pull info from a database?
✅ MCP lets the LLM call external tools and APIs in a safe and organized way.

🧠 5. Real-Time Contextual Awareness

MCP maintains memory of previous actions and decisions, helping the LLM stay on track in long conversations or complex workflows.

🛠️ Tools for Implementing MCP

You don’t have to build MCP from scratch. Several tools and platforms already support MCP-style architectures, allowing developers to bring structured, action-ready LLMs into their apps.

Pipe dream - A workflow engine designed for orchestrating complex AI tasks.
Cursor IDE- Implements a structure similar to MCP

This tools are used by me you can use other tools also as your comfort.

🌟 Thanks for reading! If you liked this, drop a 💬 comment or 🔁 share it with a friend building AI apps!

Turning Thoughts Into Actions: How Model Context Protocol(MCP) Makes LLMs Actually Useful