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Akshay 🚀 Profile picture
@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...👇 Image
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 workflowsImage
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 Image
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 🚀 Profile picture
Mar 12
Let's build a RAG app using Google DeepMind's Gemma 3 (100% local):
Google just dropped a multilingual and multimodal open-source LLM.

Today, we're building a RAG app powered by @GoogleDeepMind's Gemma3.

Tech stack:

- @Llama_Index for orchestration
- @Ollama to locally serve Gemma 3
- @Streamlit for the UI

Let's go! 🚀
The architecture diagram presented below illustrates some of the key components & how they interact with each other!

It will be followed by detailed descriptions & code for each component: Image
Read 11 tweets
Akshay 🚀 Profile picture
Mar 10
Let's build a Multimodal RAG app over complex webpages using DeepSeek's Janus-Pro (running locally):
The video depicts a multimodal RAG built with a SOTA tech stack.

We'll use:
- ColiVara's SOTA document understanding and retrieval to index webpages.
- @firecrawl_dev for reliable scrapping.
- @huggingface transformers to locally run DeepSeek Janus

Let's build it!
Here's an overview of our app:

1-2) Generate a PDF of web page screenshots with Firecrawl.
3) Index it on ColiVara for SOTA retrieval.
4-5) Query the ColiVara client to retrieve context.
6-7) Use DeepSeek Janus Pro as the LLM to generate a response.

Now let's jump into code!
Read 12 tweets
Akshay 🚀 Profile picture
Mar 8
Object oriented programming in Python, clearly explained:
We break it down to 6 important concepts:

- Object 🚘
- Class 🏗️
- Inheritance 🧬
- Encapsulation 🔐
- Abstraction 🎭
- Polymorphism 🌀

Let's take them one-by-one... 🚀 Image
1️⃣ Object 🚘

Just look around, everything you see can be treated as an object.

For instance a Car, Dog, your Laptop are all objects.

An Object can be defined using 2 things:

- Properties: that describe an object
- Behaviour: the functions that an object can perform

...👇
Read 11 tweets
Akshay 🚀 Profile picture
Mar 7
Bayes' Theorem clearly explained:
Bayes' Theorem is a cornerstone of probability theory!

It calculates the probability of an event, given that another event has occurred.

It's like updating your guess with fresh information!

Before we delve into the details, let's take a quick look at its formula: Image
Imagine you're trying to guess if it will rain today. ☔️ You start with a general belief based on the weather forecast (say, a 40% chance of rain).

This is your 'prior' probability: Image
Read 8 tweets
Akshay 🚀 Profile picture
Mar 6
Let's build a corrective RAG (CRAG) agentic workflow, step-by-step (100% local):
Before we dive in, here's a quick demo of our agentic workflow!

Tech stack:

- @Llama_Index workflows for orchestration
- @Linkup_platform for deep web search
- @Cometml's Opik to trace and monitor
- @Qdrant_engine to self-host vectorDB

Let's go! 🚀
Here's an overview of what the app does:

— First search the docs with user query
— Evaluate if the retrieved context is relevant using LLM
— Only keep the relevant context
— Do web search if needed
— Aggregate the context & generate response

Now let's jump into code!
Read 13 tweets
Akshay 🚀 Profile picture
Mar 5
AI Engineering Hub just crossed 3k stars on GitHub!

It’s 100% open-source, packed with 30+ hands-on tutorials that many would charge $1,000+ for...

Here's a small sample of what you get for free: Image
1️⃣ Multi-agent YouTube Trend Analysis App

Learn to gather trending topics using agents & transform data into actionable insights.

Check this out👇
github.com/patchy631/ai-e…
2️⃣ Agentic RAG

Build a RAG pipeline with agentic capabilities, dynamically fetch context from different sources.

Powered by @crewAIInc.

Check this out👇
github.com/patchy631/ai-e…
Read 12 tweets

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