mcp-projects
About
My Projects Repo for MCP (Model Context Protocol)
Details
- License
- MIT license
Explore
- Open‑source collection of MCP server and client examples
- Integrates with IBM watsonx.ai and Tavily search
- Agnostic to the LLM provider (adjustable with few changes)
- Includes both server and client run scripts
- Educational explanation of the Model Context Protocol
1. Clone this repo
2. Install the requirements
pip install mcp fastapi uvicorn fastapi-mcp llama-index llama-index-embeddings-huggingface llama-index-llms-langchain langchain-mcp-adapters mcp-use
3. 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>
MODEL_ID=<your watsonx.ai LLM id>
TAVILY_API_KEY=<your Tavily api key for web search>
or, use your own llm provider - its agnostic to the projects (few changes needs to be done though)
4. Experiment with different projects and files
- make sure to run the mcp servers first and then only
- run the clients
Open-Source Projects Repo for MCP (Model Context Protocol).
Steps to install and run:
1. Clone this repo
2. Install the requirements
pip install mcp fastapi uvicorn fastapi-mcp llama-index llama-index-embeddings-huggingface llama-index-llms-langchain langchain-mcp-adapters mcp-use
3. 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>
MODEL_ID=<your watsonx.ai LLM id>
TAVILY_API_KEY=<your Tavily api key for web search>
or, use your own llm provider - its agnostic to the projects (few changes needs to be done though)
4. Experiment with different projects and files
- make sure to run the mcp servers first and then only
- run the clients
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 NavigationTraditional 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
Read this medium article for comprehensive understanding of MCP: Understanding Model Context Protocol (MCP): A Layman’s Guide
Do make Pull Requests to contribute to this asset ✨
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