What is Model Context Protocol Mcp Tutorial Project

The Model Context Protocol (MCP) Tutorial Project is a demonstration of how to structure prompts for Large Language Models (LLMs) using the Model Context Protocol (MCP). It utilizes technologies like LangChain and FAISS to enhance prompt quality and includes visualizations of the prompt structure.

Use cases

Use cases for the Model Context Protocol include developing chatbots, enhancing search capabilities, creating educational tools, and any application that benefits from improved interaction with LLMs.

How to use

To use the Model Context Protocol Tutorial Project, clone the repository or copy the notebook to Google Colab. Install the required dependencies using pip, and then follow the provided instructions to set up the context, embed content, and perform retrieval QA with the mock LLM.

Key features

Key features of the Model Context Protocol include a systematic approach to prompt crafting, segmentation of input into task descriptions, constraints, tools, and examples, and the ability to visualize prompt structures for better understanding.

Where to use

The Model Context Protocol can be applied in various fields such as natural language processing, AI development, and any domain where effective prompt engineering for LLMs is required.

Model Context Protocol (MCP) Tutorial Project

This project demonstrates how to use the Model Context Protocol (MCP) to structure prompts for Large Language Models (LLMs) using LangChain, FAISS, and a mock LLM. It walks through how MCP improves prompt quality and includes a visualization of prompt structure.

What is MCP?

Model Context Protocol is a systematic approach for crafting LLM prompts. It segments input into:

• ✅ Task Description
• ✅ Constraints
• ✅ Tools
• ✅ Examples

This helps LLMs interpret user intent more reliably and produce higher quality output.

Technologies Used

• LangChain
• FAISS
• Transformers
• Matplotlib + Seaborn
• Python 3.11+

Project Structure

• Context Setup: Define MCP content in memory
• Vector Store: Embed content using HuggingFace MiniLM and store in FAISS
• Fake LLM: Use LangChain’s mock LLM for quick testing
• RetrievalQA: Ask questions using context-aware retrieval
• Visualization: Bar chart showing simulated MCP prompt structure

How to Run

1. Clone the repo or copy the notebook to Colab

2. Install dependencies:

   pip install -q langchain langchain-community faiss-cpu matplotlib seaborn
   
   
   

Sample Output
Response: MCP structures prompts with task, constraints, tools, and examples.

Notes
This uses a FakeListLLM for simplicity. Replace with OpenAI, HuggingFaceHub, or any production-grade LLM for real use.

FAISS is used here for local, fast retrieval. You can swap with Chroma or a cloud vector DB.

📄 License
MIT © 2025