Share this page!

Enter URL or ID to Unroll

You can paste full URL like: https://x.com/threadreaderapp/status/1644127596119195649
or just the ID like: 1644127596119195649

How to get URL link on X (Twitter) App

  1. On the Twitter thread, click on or icon on the bottom
  2. Click again on or Share Via icon
  3. Click on Copy Link to Tweet
  4. Paste it above and click "Unroll Thread"!
  5. More info at Twitter Help
Akshay 🚀 Profile picture
@akshay_pachaar
Mar 13 • 11 tweets • 3 min read • Read on X
Scrolly
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!

• • •

Missing some Tweet in this thread? You can try to force a refresh
　

Keep Current with Akshay 🚀

Akshay 🚀 Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @akshay_pachaar

Akshay 🚀 Profile picture
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 🚀 Profile picture
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
Akshay 🚀 Profile picture
Sep 9
6 GitHub repositories that will give you superpowers as an AI Engineer:
You can use these 6 open-source repos/tools for:

- building an enterprise-grade RAG solution
- build and deploy multi-agent workflows
- finetune 100+ LLMs
- and more...

Let's learn more about them one by one: Image
1️⃣ Sim AI

A drag-and-drop UI to build AI agent workflows!

Sim AI is a lightweight, user-friendly platform that makes creating AI agent workflows accessible to everyone.

Supports all major LLMs, MCP servers, vectorDBs, etc.

100% open-source.

🔗 github.com/simstudioai/sim
Read 10 tweets
Akshay 🚀 Profile picture
Sep 7
8 key skills to become a full-stack AI Engineer:
Production-grade AI systems demand deep understanding of how LLMs are engineered, deployed, and optimized.

Here are the 8 pillars that define serious LLM development:

Let's dive in! 🚀
1️⃣ Prompt engineering

Prompt engineering is far from dead!

The key is to craft structured prompts that reduce ambiguity and result in deterministic outputs.

Treat it as engineering, not copywriting! ⚙️

Here's something I published on JSON prompting:
Read 12 tweets
Akshay 🚀 Profile picture
Sep 6
K-Means has two major problems:

- The number of clusters must be known
- It doesn't handle outliers

Here’s an algorithm that addresses both issues:
Introducing DBSCAN, a density-based clustering algorithm.

Simply put, DBSCAN groups together points in a dataset that are close to each other based on their spatial density.

It's very easy to understand, just follow along ...👇 Image
DBSCAN has two important parameters.

1️⃣ Epsilon (eps):

`eps`: represents the maximum distance between two points for them to be considered part of the same cluster.

Points within this distance of each other are considered to be neighbours.

Check this out 👇 Image
Read 9 tweets
Akshay 🚀 Profile picture
Sep 4
Let's build a reasoning LLM, from scratch (100% local):
Today, we're going to learn how to turn any model into a reasoning powerhouse.

We'll do so without any labeled data or human intervention, using Reinforcement Finetuning (GRPO)!

Tech stack:

- @UnslothAI for efficient fine-tuning
- @HuggingFace TRL to apply GRPO

Let's go! 🚀
What is GRPO?

Group Relative Policy Optimization is a reinforcement learning method that fine-tunes LLMs for math and reasoning tasks using deterministic reward functions, eliminating the need for labeled data.

Here's a brief overview of GRPO before we jump into code:
Read 12 tweets

Did Thread Reader help you today?

Support us! We are indie developers!

This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Don't want to be a Premium member but still want to support us?

Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal

Or Donate anonymously using crypto!

Ethereum

0xfe58350B80634f60Fa6Dc149a72b4DFbc17D341E copy

Bitcoin

3ATGMxNzCUFzxpMCHL5sWSt4DVtS8UqXpi copy

Thank you for your support!

Follow Us!

Send Email!

:(