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Akshay 🚀

@akshay_pachaar

Mar 13 • 11 tweets • 3 min read • Read on X

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Model Context Protocol (MCP), clearly explained:

MCP is like a USB-C port for your AI applications.

Just as USB-C offers a standardized way to connect devices to various accessories, MCP standardizes how your AI apps connect to different data sources and tools.

Let's dive in! 🚀

At its core, MCP follows a client-server architecture where a host application can connect to multiple servers.

Key components include:

- Host
- Client
- Server

Here's an overview before we dig deep 👇

The Host and Client:

Host: An AI app (Claude desktop, Cursor) that provides an environment for AI interactions, accesses tools and data, and runs the MCP Client.

MCP Client: Operates within the host to enable communication with MCP servers.

Next up, MCP server...👇

The Server

A server exposes specific capabilities and provides access to data.

3 key capabilities:

- Tools: Enable LLMs to perform actions through your server
- Resources: Expose data and content from your servers to LLMs
- Prompts: Create reusable prompt templates and workflows

The Client-Server Communication

Understanding client-server communication is essential for building your own MCP client-server.

Let's begin with this illustration and then break it down step by step... 👇

1️⃣ & 2️⃣: capability exchange

client sends an initialize request to learn server capabilities.

server responds with its capability details.

e.g., a Weather API server provides available `tools` to call API endpoints, `prompts`, and API documentation as `resource`.

3️⃣ Notification

Client then acknowledgment the successful connection and further message exchange continues.

Before we wrap, one more key detail...👇

Unlike traditional APIs, the MCP client-server communication is two-way.

Sampling, if needed, allows servers to leverage clients' AI capabilities (LLM completions or generations) without requiring API keys.

While clients to maintain control over model access and permissions

I hope this clarifies what MCP does.

In the future, I'll explore creating custom MCP servers and building hands-on demos around them.

Over to you! What is your take on MCP and its future?

That's a wrap!

If you enjoyed this breakdown:

Follow me → @akshay_pachaar ✔️

Every day, I share insights and tutorials on LLMs, AI Agents, RAGs, and Machine Learning!

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More from @akshay_pachaar

Akshay 🚀

@akshay_pachaar

Oct 6

You're in a Research Scientist interview at OpenAI.

The interviewer asks:

"How would you expand the context length of an LLM from 2K to 128K tokens?"

You: "I will fine-tune the model on longer docs with 128K context"

Interview over.

Here's what you missed:

Extending the context window isn't just about larger matrices.

In a traditional transformer, expanding tokens by 8x increases memory needs by 64x due to the quadratic complexity of attention. Refer to the image below!

So, how do we manage it?

continue...👇

1) Sparse Attention

It limits the attention computation to a subset of tokens by:

- Using local attention (tokens attend only to their neighbors).
- Letting the model learn which tokens to focus on.

But this has a trade-off between computational complexity and performance.

Read 10 tweets

Akshay 🚀

@akshay_pachaar

Sep 25

Local MCP clients are so underrated!

Everyone's using Cursor, Claude Desktop, and ChatGPT as MCP hosts, but if you're building your own apps that support MCP, you need custom clients.

Here's the problem: Writing MCP clients from scratch is painful and time-consuming.

Today, I'm showing you how to build custom MCP clients in minutes, not hours.

To prove this, I built a fully private, ultimate AI assistant that can:

- Connects to any MCP server
- Automates browser usage
- Scrapes web data seamlessly
- Controls the terminal of my computer
- Processes images, audio, and documents
- Remembers everything with knowledge graphs

The secret? mcp-use — a 100% open-source framework that makes MCP integration trivial.

Building custom MCP agents takes 3 steps:

1. Define your MCP server configuration
2. Connect any LLM with the MCP client
3. Deploy your agent

That's it. No complex setup, no proprietary dependencies.

The best part? Everything runs locally. Your data stays private, and you control the entire stack.

Full breakdown with code...👇

Let's break this down by exploring each integration and understanding how it works, using code and illustrations:

1️⃣ Stagehand MCP server

We begin by allowing our Agent to control a browser, navigate web pages, take screenshots, etc., using @Stagehanddev MCP.

Below, I asked a weather query, and the Agent autonomously responded to it by initiating a browser session.

Check this👇

Read 11 tweets

Akshay 🚀

@akshay_pachaar

Sep 23

Context engineering, clearly explained!

Everybody is talking about context engineering, but no one tells you what it actually means.

Today, I'll explain everything you need to know about context engineering in a step-by-step manner.

Here's an illustrated guide:

So, what is context engineering?

It’s the art and science of delivering the right information, in the right format, at the right time, to your LLM.

Here's a quote by Andrej Karpathy on context engineering...👇

To understand context engineering, it's essential to first understand the meaning of context.

Agents today have evolved into much more than just chatbots.

The graphic below summarizes the 6 types of contexts an agent needs to function properly.

Check this out 👇

Read 11 tweets

Akshay 🚀

@akshay_pachaar

Sep 19

We've all dealt with activation functions while working with neural nets.

- Sigmoid
- Tanh
- ReLu & Leaky ReLu
- Gelu

Ever wondered why they are so important❓🤔

Let me explain... 👇

Before we proceed, I want you to understand something!

You can think of a layer in a neural net as a function & multiple layers make the network a composite function.

Now, a composite function consisting of individual linear functions is also linear.

Check this out👇

We have a simple neural net that does binary classification.

Scenario 1:

- Linear decision boundary
- Linear Activation function

Observe how the neural net is able to quickly learn & loss converges to zero.

Watch this 👇

Read 7 tweets

Akshay 🚀

@akshay_pachaar

Sep 12

10 MCP, AI Agents & LLM visual explainers:

(don't forget to bookmark 🔖)

1️⃣ MCP

MCP is a standardized way for LLMs to access tools via a client–server architecture.

Think of it as a JSON schema with agreed-upon endpoints.

Anthropic said, "Hey, let's all use the same JSON format when connecting AI to tools" and everyone said "Sure."

Check this👇

2️⃣ MCP vs Function calling for LLMs

Before MCPs became popular, AI workflows relied on traditional Function Calling for tool access. Now, MCP is standardizing it for Agents/LLMs.

The visual covers how Function Calling & MCP work under the hood.

Check this out👇

Read 12 tweets

Akshay 🚀

@akshay_pachaar

Sep 11

I've put 100+ MCP apps into production!

There's one rule you can not miss if you want to do the same!

Here's the full breakdown (with code):

There are primarily 2 factors that determine how well an MCP app works:

- If the model is selecting the right tool?
- And if it's correctly preparing the tool call?

Today, let's learn how to evaluate any MCP workflow using @deepeval's MCP evaluations (open-source).

Let's go!

Here's the workflow:

- Integrate the MCP server with the LLM app.
- Send queries and log tool calls, tool outputs in DeepEval.
- Once done, run the eval to get insights on the MCP interactions.

Now let's dive into the code for this!

Read 13 tweets

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