What is MCP?
MCP (Model Context Protocol) is a standardized framework that enhances AI models by providing them with richer context about their environment, user preferences, and conversation history. It addresses the limitations of current AI systems that struggle with maintaining context over longer interactions.
How to use MCP?
To use MCP, follow these steps:
- Clone the repository from GitHub.
- Install the required Python packages using pip.
- Create a .env file with your API credentials.
- Experiment with different projects and files to see how MCP can enhance AI interactions.
Key features of MCP?
- Standardized method for providing context to AI models.
- Ability to maintain conversation history beyond the typical context window.
- Flexibility to work with various LLM providers.
- Enhances user experience by reducing the need for repetitive explanations.
Use cases of MCP?
- Improving customer service interactions by maintaining context over multiple exchanges.
- Enhancing virtual assistants to remember user preferences and past interactions.
- Enabling more complex AI applications that require a deeper understanding of user context.
FAQ from MCP?
- What is the main benefit of using MCP?
MCP allows AI systems to remember more context, leading to smoother and more natural interactions.
- Is MCP compatible with all AI models?
Yes, MCP is designed to be agnostic and can work with various AI models and providers.
- How do I contribute to the MCP project?
You can contribute by making pull requests on the GitHub repository.
mcp-projects
My Projects Repo for MCP (Model Context Protocol)
Steps to install and run:
- Clone this repo
- Install the requirements
pip install mcp pip install fastapi pip install uvicorn pip install fastapi-mcp pip install llama-index pip install llama-index-embeddings-huggingface pip install llama-index-llms-langchain pip install langchain-mcp-adapters - Make a .env file in the root folder with the following credentials:
API_KEY=<IBM_cloud_API_Key> PROJECT_ID=<Watsonx_Project_id> IBM_CLOUD_URL=<IBM cloud url> or, use your own llm providers - its agnostic to the projects - Experiment with different projects and files
What is Model Context Protocol (MCP)?
At its core, MCP is a standardized way for applications to provide AI models with richer context about their environment, user preferences, and conversation history. Think of it as a smart, structured way to feed memory and context to AI systems.
The Problem MCP Solves
Current AI systems have limited "working memory" - they can only see a certain amount of conversation history at once (their "context window"). Imagine trying to have a conversation with someone who only remembers the last few exchanges:
- You: "Remember that project we discussed last week about optimizing the supply chain?"
- AI without good context: "I don't recall that specific discussion. Could you remind me of the details?" This limitation forces users to constantly re-explain things, leading to frustrating interactions. MCP aims to solve this by creating a structured method for maintaining and accessing context.
Some Analogies
1. GPS Navigation
Traditional AI context management is like giving someone directions one turn at a time, without showing them the full map. If they forget a step, the journey breaks down.
MCP is like a GPS navigation system that:
- Knows your destination
- Remembers your preferred routes
- Adjusts based on real-time conditions
- Always knows exactly where you are in the journey
Do make Pull Requests to contribute to this asset ✨
