mcp-projects

by SrGrace

162 downloads Not rated yet MIT license

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My Projects Repo for MCP (Model Context Protocol)

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License
MIT license

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- 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.

MCP Structure

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

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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