docs: add readme

Author: Henrrypg

backend/openedx_ai_extensions/mcp/README.md

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+# Model Context Protocol (MCP) - Proof of Concept
+
+This directory contains a **proof of concept (MVP)** demonstrating how Model Context Protocol (MCP) works. This is **NOT** the full OpenEdx AI Extensions integration yet, but rather a working example to understand MCP concepts and test the infrastructure.
+
+## Overview
+
+This MVP demonstrates the MCP architecture and workflow using a simple dice-rolling example. The implementation consists of three main components:
+
+1. **`server.py`** - A FastMCP server with an example tool (`roll_dice`)
+2. **`run_server.py`** - Script to run the MCP server in HTTP mode
+3. **`client_example.py`** - Example client showing how to interact with the MCP server
+
+**Note:** The actual OpenEdx-specific tools and integration will be implemented in future iterations. This MVP focuses on validating the MCP infrastructure and communication patterns.
+
+## Architecture
+
+The current implementation uses:
+- **FastMCP** - A framework for building MCP servers
+- **Streamable HTTP** transport - Allows the server to be exposed via HTTP
+- **LiteLLM** - For integrating the MCP server with language models
+
+### Example Tool: `roll_dice`
+
+The server currently implements a simple example tool that rolls dice. **This is a demonstration tool only** to show how MCP tools work. This pattern will later be extended to implement OpenEdx-specific operations like course management, user administration, content creation, etc.
+
+## Setup
+
+### Prerequisites
+
+Install the required dependencies:
+
+```bash
+pip install fastmcp litellm openai
+```
+
+### Environment Variables
+
+Set your OpenAI API key:
+
+```bash
+export OPENAI_API_KEY="your_openai_api_key_here"
+```
+
+Or update it directly in `client_example.py`.
+
+## Running the Server
+
+### Step 1: Start the MCP Server
+
+Run the server locally on port 9001:
+
+```bash
+python run_server.py
+```
+
+The server will start and listen on `http://127.0.0.1:9001/mcp`.
+
+### Step 2: Expose the Server with ngrok
+
+Since the MCP protocol requires a publicly accessible endpoint for certain use cases, you need to expose your local server using ngrok:
+
+```bash
+# Install ngrok if you haven't already
+# Visit https://ngrok.com/ to download and set up
+
+# Expose port 9001
+ngrok http 9001
+```
+
+ngrok will provide you with a public URL like:
+```
+https://abc123.ngrok-free.app
+```
+
+**Important**: Copy the ngrok URL (including the subdomain) as you'll need it for the client configuration.
+
+### Step 3: Update the Client Configuration
+
+Edit `client_example.py` and update the `server_url` with your ngrok URL:
+
+```python
+tools=[
+    {
+        "type": "mcp",
+        "server_label": "dice_server",
+        "server_url": "https://<your_ngrok_subdomain>.ngrok-free.app/mcp/",
+        "require_approval": "never",
+    },
+],
+```
+
+Replace `<your_ngrok_subdomain>` with your actual ngrok subdomain (e.g., `abc123.ngrok-free.app`).
+
+### Step 4: Run the Client Example
+
+In a new terminal (while the server and ngrok are still running):
+
+```bash
+python client_example.py
+```
+
+## Testing Workflow
+
+Here's the complete workflow for testing:
+
+1. **Terminal 1** - Start the MCP server:
+   ```bash
+   cd backend/openedx_ai_extensions/mcp
+   python run_server.py
+   ```
+
+2. **Terminal 2** - Expose with ngrok:
+   ```bash
+   ngrok http 9001
+   ```
+   Copy the ngrok URL from the output.
+
+3. **Terminal 3** - Run the client:
+   ```bash
+   # Update client_example.py with your ngrok URL first
+   python client_example.py
+   ```
+
+## Expected Output
+
+When running the client, you should see:
+
+1. List of available tools from the MCP server
+2. The AI model response after using the `roll_dice` tool
+
+Example:
+```
+Available resources: ['roll_dice']
+Response: <LiteLLM response object with dice roll results>
+```