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mcp-projects

mcp-projects

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github.com/SrGraceDeveloper Tools3Updated 63w ago
Repository

About mcp-projects

mcp-projects is an open-source repository of MCP projects and files for experimentation. Its setup instructions cover installing dependencies and configuring environment variables, and specify running servers before clients.

Key features

  1. 01MCP projects and files to explore
  2. 02Dependency installation instructions
  3. 03Credentials configured through environment variables
  4. 04Separate server and client run steps

Use cases

  1. 01Experiment with projects in the repository
  2. 02Set up a local environment to run servers and clients

MCP support

The repository contains MCP server and client projects; its instructions say to start servers before running clients.

#mcp#mcp-server#mcp-client

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

mcp-projects

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 ✨

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.