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Akshay 🚀 Profile picture
@akshay_pachaar
Jun 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?
If you found it insightful, reshare with your network.

Find me → @akshay_pachaar ✔️
For more insights and tutorials on LLMs, AI Agents, and Machine Learning!

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More from @akshay_pachaar

Akshay 🚀 Profile picture
Jun 11
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
Jun 5
Self-attention in LLMs, clearly explained:
Before we start a quick primer on tokenization!

Raw text → Tokenization → Embedding → Model

Embedding is a meaningful representation of each token (roughly a word) using a bunch of numbers.

This embedding is what we provide as an input to our language models.

Check this👇 Image
The core idea of Language modelling is to understand the structure and patterns within language.

By modeling the relationships between words (tokens) in a sentence, we can capture the context and meaning of the text. Image
Read 9 tweets
Akshay 🚀 Profile picture
Jun 3
Let's build an MCP-powered Agentic RAG (100% local):
Below, we have an MCP-powered Agentic RAG that searches a vector database and falls back to web search if needed.

To build this, we'll use:
- @firecrawl_dev search endpoint for web search.
- @qdrant_engine as the vector DB.
- @cursor_ai as the MCP client.

Let's build it!
Here's how it works:

1) The user inputs a query through the MCP client (Cursor).
2-3) The client contacts the MCP server to select a relevant tool.
4-6) The tool output is returned to the client to generate a response.

Let's implement this!
Read 12 tweets
Akshay 🚀 Profile picture
Jun 1
Function calling & MCP for LLMs, clearly explained (with visuals):
Before MCPs became popular, AI workflows relied on traditional Function Calling for tool access. Now, MCP is standardizing it for Agents/LLMs.

The visual below explains how Function Calling and MCP work under the hood.

Let's learn more!
In Function Calling:

- The LLM receives a prompt.
- The LLM decides the tool.
- The programmer implements a procedure to accept a tool call request and prepare a function call.
- A backend service executes the tool.

Let's implement this to understand better!
Read 10 tweets
Akshay 🚀 Profile picture
May 30
Let's build an MCP server that connects to 200+ data sources (100% local):
Before we dive in, here's a quick demo of what we're building!

Tech stack:

- @MindsDB to power our unified MCP server
- @cursor_ai as the MCP host
- @Docker to self-host the server

Let's go! 🚀
Here's the workflow:

- User submits a query
- Agent connects to MindsDB MCP server to find tools
- Selects appropriate tool based on user query and call it
- Finally, returns a contextually relevant response

Now, let's dive into the code!
Read 12 tweets
Akshay 🚀 Profile picture
May 29
KV caching in LLMs, clearly explained (with visuals):
KV caching is a technique used to speed up LLM inference.

Before understanding the internal details, look at the inference speed difference in the video:

- with KV caching → 9 seconds
- without KV caching → 42 seconds (~5x slower)

Let's dive in!
To understand KV caching, we must know how LLMs output tokens.

- Transformer produces hidden states for all tokens.
- Hidden states are projected to vocab space.
- Logits of the last token is used to generate the next token.
- Repeat for subsequent tokens.

Check this👇
Read 11 tweets

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