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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

Jun 25

Let's generate our own LLM fine-tuning dataset (100% local):

Before we begin, here's what we're doing today!

We'll cover:
- What is instruction fine-tuning?
- Why is it important for LLMs?

Finally, we'll create our own instruction fine-tuning dataset.

Let's dive in!

Once an LLM has been pre-trained, it simply continues the sentence as if it is one long text in a book or an article.

For instance, check this to understand how a pre-trained LLM behaves when prompted 👇

Read 12 tweets

Akshay 🚀

@akshay_pachaar

Jun 22

Let's build a real-time Voice RAG Agent, step-by-step:

Before we begin, here's a quick demo of what we're building

Tech stack:

- @Cartesia_AI for SOTA text-to-speech
- @AssemblyAI for speech-to-text
- @LlamaIndex to power RAG
- @livekit for orchestration

Let's go! 🚀

Here's an overview of what the app does:

1. Listens to real-time audio
2. Transcribes it via AssemblyAI
3. Uses your docs (via LlamaIndex) to craft an answer
4. Speaks that answer back with Cartesia

Now let's jump into code!

Read 11 tweets

Akshay 🚀

@akshay_pachaar

Jun 21

Let's build an MCP-powered audio analysis toolkit:

Before we dive in, here's a demo of what we're building!

Tech stack:
- @AssemblyAI for transcription and audio analysis.
- Claude Desktop as the MCP host.
- @streamlit for the UI

Let's build it!

Here's our workflow:

- User's audio input is sent to AssemblyAI via a local MCP server.
- AssemblyAI transcribes it while providing the summary, speaker labels, sentiment, and topics.
- Post-transcription, the user can also chat with audio.

Let's implement this!

Read 11 tweets

Akshay 🚀

@akshay_pachaar

Jun 19

AI agents can finally talk to your frontend!

The AG-UI Protocol bridges the critical gap between AI agents and frontend apps, making human-agent collaboration seamless.

MCP: Agents to tools
A2A: Agents to agents
AG-UI: Agents to users

100% open-source.

Here's the official GitHub repo for @CopilotKit's AG-UI:

(don't forget to star 🌟)github.com/ag-ui-protocol…

Here's a really good illustration of how it works!

Key features:

🤝 Works with LangGraph, LlamaIndex, Agno, CrewAI & AG2
🎯 Event-based protocol with 16 standard event types
💬 Real-time agentic chat with streaming
🧑‍💻 Human-in-the-loop collaboration
💬 ChatUI & Generative UI

Read 4 tweets

Akshay 🚀

@akshay_pachaar

Jun 16

Top 4 open-source LLM finetuning libraries!

From single-GPU “click-to-tune” notebooks to trillion-param clusters, these four libraries cover every LLM finetuning scenario.

Understand which one to use, & when...👇

1️⃣ Unsloth

Unsloth makes fine-tuning easy and fast, turning a mid-range GPU into a powerhouse with a simple Colab or Kaggle notebook.

Perfect for hackers and small teams using 12–24 GB GPUs needing quick LoRA experiments without DeepSpeed configs or clusters

Check this out👇
github.com/unslothai/unsl…

2️⃣ Axolotl

Axolotl keeps your entire pipeline in one YAML file—write once, reuse from data prep to serving.

Perfect for teams that crave reproducibility and want to toggle advanced recipes by flipping a YAML switch.

Check this out👇
github.com/axolotl-ai-clo…

Read 6 tweets