diff --git a/content/learning-paths/servers-and-cloud-computing/mcp-ai-agent/_index.md b/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
similarity index 69%
rename from content/learning-paths/servers-and-cloud-computing/mcp-ai-agent/_index.md
rename to content/learning-paths/cross-platform/mcp-ai-agent/_index.md
index d7d66b6818..4999c1eebd 100644
--- a/content/learning-paths/servers-and-cloud-computing/mcp-ai-agent/_index.md
+++ b/content/learning-paths/cross-platform/mcp-ai-agent/_index.md
@@ -1,5 +1,5 @@
 ---
-title: Deploy an MCP server on a Raspberry Pi 5 and interact with it using the AI agent
+title: Deploy an MCP server on a Raspberry Pi 5 and interact with it using an AI agent
 
 draft: true
 cascade:
@@ -7,16 +7,16 @@ cascade:
     
 minutes_to_complete: 30
 
-who_is_this_for: This Learning Path targets LLM and IoT developers who already know their way around Large Language Model (LLM) concepts and networking. It walks you through deploying a lightweight Model Context Protocol (MCP) server on a Raspberry Pi 5 and shows you how to interact with it via the OpenAI-Agent SDK.
+who_is_this_for: This Learning Path targets LLM and IoT developers who are familiar with Large Language Model (LLM) concepts and networking. You will learn how to deploy a lightweight Model Context Protocol (MCP) server on a Raspberry Pi 5 and interact with it via the OpenAI-Agent SDK.
 
 learning_objectives: 
-    - Understand how to Deploy a lightweight Model Context Protocol (MCP) server on Raspberry Pi 5
+    - Deploy a lightweight Model Context Protocol (MCP) server on Raspberry Pi 5
     - Design and register custom tools for the AI Agent
     - Create custom endpoints
-    - Learn uv — a fast, efficient Python package manager
+    - Learn about uv — a fast, efficient Python package manager
 
 prerequisites:
-    - Rapberry Pi
+    - A [Raspberry Pi 5](https://www.raspberrypi.com/products/raspberry-pi-5/)
     - Basic understanding of Python and prompt engineering.
     - Understanding of LLM and AI Agent fundamentals
 
@@ -34,6 +34,11 @@ tools_software_languages:
 
 operatingsystems:
     - Linux
+### Cross-platform metadata only
+shared_path: true
+shared_between:
+    - iot
+    - embedded-and-microcontrollers
 
 further_reading:
     - resource:
diff --git a/content/learning-paths/servers-and-cloud-computing/mcp-ai-agent/_next-steps.md b/content/learning-paths/cross-platform/mcp-ai-agent/_next-steps.md
similarity index 100%
rename from content/learning-paths/servers-and-cloud-computing/mcp-ai-agent/_next-steps.md
rename to content/learning-paths/cross-platform/mcp-ai-agent/_next-steps.md
diff --git a/content/learning-paths/servers-and-cloud-computing/mcp-ai-agent/intro-to-mcp-uv.md b/content/learning-paths/cross-platform/mcp-ai-agent/intro-to-mcp-uv.md
similarity index 54%
rename from content/learning-paths/servers-and-cloud-computing/mcp-ai-agent/intro-to-mcp-uv.md
rename to content/learning-paths/cross-platform/mcp-ai-agent/intro-to-mcp-uv.md
index 1fe800e047..e2cf7da879 100644
--- a/content/learning-paths/servers-and-cloud-computing/mcp-ai-agent/intro-to-mcp-uv.md
+++ b/content/learning-paths/cross-platform/mcp-ai-agent/intro-to-mcp-uv.md
@@ -14,11 +14,11 @@ Think of it as USB-C for AI: once a tool or data source speaks MCP, any complian
 ### Why use MCP?
 - **Plug-and-play integrations:** A growing catalog of pre-built MCP servers (filesystem, shell, vector stores, web-scraping, etc.) gives your agent instant super-powers with zero custom glue code.
 
-- **Model/vendor agnostic:** Because the protocol lives outside the model, you can swap GPT-4, Claude, or your own fine-tuned model without touching the integration layer.
+- **Model/vendor agnostic:** Because the protocol lives outside the model, you can swap models like GPT-4, Claude, or your own fine-tuned model without touching the integration layer.
 
 - **Security by design:** MCP encourages running servers inside your own infrastructure, so sensitive data never leaves the perimeter unless you choose.
 
-- **Cross-ecosystem momentum:** Recent roll-outs—from an official C# SDK to Wix’s production MCP server and Microsoft’s Azure support—show the spec is gathering real-world traction.
+- **Cross-ecosystem momentum:** Recent roll-outs—from an official C# SDK to Wix’s production MCP server and Microsoft’s Azure support—show the MCP spec is gathering real-world traction.
 
 ### High-level architecture
 ![mcp server](./mcp.png)
@@ -32,51 +32,3 @@ Think of it as USB-C for AI: once a tool or data source speaks MCP, any complian
 Learn more about AI Agents in the [AI Agent on CPU learning path](https://learn.arm.com/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/).
 {{% /notice %}}
 
-## UV: The Fast, All-in-One Python Package Manager
-
-**uv** is a next-generation, Rust-based package manager that unifies pip, virtualenv, Poetry, and more—offering 10×–100× faster installs, built-in virtual environment handling, robust lockfiles, and full compatibility with the Python ecosystem.
-
-### Install uv
-- macOS / Linux  
-```bash
-curl -LsSf https://astral.sh/uv/install.sh | sh
-```
-- Windows
-```bash
-powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
-```
-
-### Initialize a Project
-1. Create & enter your project folder:
-```bash
-mkdir my-project && cd my-project
-```
-2. Run
-```bash
-uv init
-```
-
-This scaffolds:
-- .venv/ (auto-created virtual environment)
-- pyproject.toml (project metadata & dependencies)
-- .python-version (pinned interpreter)
-- README.md, .gitignore, and a sample main.py
-
-### Install Dependencies
-- Add one or more packages to your project:
-```bash
-uv add requests numpy pandas
-```
-> Updates both pyproject.toml and the lockfile (uv.lock)
-
-- Remove a package (and its unused sub-deps):
-```bash
-uv remove numpy
-```
-
-- Install from an existing requirements.txt (e.g. when migrating):
-```bash:
-uv pip install -r requirements.txt
-```
-
-All installs happen inside your project’s .venv, and UV’s lockfile guarantees repeatable environments.
\ No newline at end of file
diff --git a/content/learning-paths/servers-and-cloud-computing/mcp-ai-agent/mcp-client.md b/content/learning-paths/cross-platform/mcp-ai-agent/mcp-client.md
similarity index 71%
rename from content/learning-paths/servers-and-cloud-computing/mcp-ai-agent/mcp-client.md
rename to content/learning-paths/cross-platform/mcp-ai-agent/mcp-client.md
index 843f17e587..d99f3d61ca 100644
--- a/content/learning-paths/servers-and-cloud-computing/mcp-ai-agent/mcp-client.md
+++ b/content/learning-paths/cross-platform/mcp-ai-agent/mcp-client.md
@@ -1,31 +1,49 @@
 ---
-title: Build & Run an AI Agent on Your Workstation
+title: Build & Run an AI Agent on your development machine
 weight: 4
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
+In this section you will learn how to setup an AI Agent on your development machine. You will then connect your MCP server running on the Raspberry Pi 5 to it.
+
+These commands were tested on an Linux Arm development machine. 
+
 ### Create an AI Agent and point it at your Pi's MCP Server
-1. Bootstrap the Agent Project
+1. Install `uv` on your development machine:
+
+```bash
+curl -LsSf https://astral.sh/uv/install.sh | sh
+```
+2. Create a directory for the Agent:
 ```bash
-# create & enter folder
 mkdir mcp-agent && cd mcp-agent
 ```
-2. scaffold with **uv**
+3. Setup the directory to use `uv`:
 ```bash
 uv init
 ```
-3. install **OpenAI Agents SDK** + **dotenv**
+
+This command adds:
+- .venv/ (auto-created virtual environment)
+- pyproject.toml (project metadata & dependencies)
+- .python-version (pinned interpreter)
+- README.md, .gitignore, and a sample main.py
+
+4. Install **OpenAI Agents SDK** + **dotenv**
 ```bash
 uv add openai-agents python-dotenv
 ```
-4. Create a `.env` file with your OpenAI key:
+5. Create a `.env` file with your OpenAI key:
 ```bash
 echo -n "OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>" > .env
 ```
 
-### Write the Agent Client (main.py)
+### Write the Python script for the Agent Client
+
+Use a file editor of your choice and replace the content of the sample `main.py` with the content shown below:
+
 ```python
 import asyncio, os
 from dotenv import load_dotenv
@@ -38,7 +56,7 @@ from agents import Agent, Runner, set_default_openai_key
 from agents.mcp import MCPServerSse
 from agents.model_settings import ModelSettings
 
-async def run(mcp_server: list[MCPServer]):
+async def run(mcp_server: list[MCPServerSse]):
     set_default_openai_key(os.getenv("OPENAI_API_KEY"))
 
     agent = Agent(
@@ -70,10 +88,14 @@ if __name__ == "__main__":
 ```
 
 ### Execute the Agent
+
+You are now ready to the run the agent and test it with your running MCP server:
+
+Run the `main.py` python script:
 ```bash
 uv run main.py
 ```
-You should see output like:
+The output should look like:
 ```output
 Running: What is the CPU temperature?
 Response: The current CPU temperature is 48.8°C.
diff --git a/content/learning-paths/servers-and-cloud-computing/mcp-ai-agent/mcp-server.md b/content/learning-paths/cross-platform/mcp-ai-agent/mcp-server.md
similarity index 50%
rename from content/learning-paths/servers-and-cloud-computing/mcp-ai-agent/mcp-server.md
rename to content/learning-paths/cross-platform/mcp-ai-agent/mcp-server.md
index 9de994484a..93e163b9d6 100644
--- a/content/learning-paths/servers-and-cloud-computing/mcp-ai-agent/mcp-server.md
+++ b/content/learning-paths/cross-platform/mcp-ai-agent/mcp-server.md
@@ -6,26 +6,25 @@ weight: 3
 layout: learningpathall
 ---
 
-## Expose Raspberry Pi MCP Server via ngrok
+## Setup an MCP Server on Raspberry Pi 5
 
-This guide shows you how to:
+In this section you will learn how to:
 
-1. Install **uv** (the Rust-powered Python manager)  
-2. Bootstrap a simple **MCP** server on your Raspberry Pi that reads the CPU temperature and searches the weather data
-3. Expose it to the internet with **ngrok**
-
-### Prerequisites
-
-- A **Raspberry Pi 5** (or other ARMv8 Pi) running Raspberry Pi OS (64-bit)  
-- Basic familiarity with Python and the terminal  
+1. Install uv (the Rust-powered Python package manager)  
+2. Bootstrap a simple MCP server on your Raspberry Pi 5 that reads the CPU temperature and searches the weather data
+3. Expose the MCP server to the internet with **ngrok**
 
+You will run all the commands shown below on your Raspberry Pi 5 running Raspberry Pi OS (64-bit)  
 
 #### 1. Install uv
-On Raspberry Pi Terminal:
+On Raspberry Pi Terminal, install `uv`:
 ```bash
 curl -LsSf https://astral.sh/uv/install.sh | sh
 ```
 
+**uv** is a next-generation, Rust-based package manager that unifies pip, virtualenv, Poetry, and more—offering 10×–100× faste
+r installs, built-in virtual environment handling, robust lockfiles, and full compatibility with the Python ecosystem.
+
 {{% notice Note %}}
 After the script finishes, restart your terminal so that the uv command is on your PATH.
 {{% /notice %}}
@@ -36,22 +35,28 @@ After the script finishes, restart your terminal so that the uv command is on yo
 mkdir mcp
 cd mcp
 ```
-2. Initialize with uv (this creates pyproject.toml, .venv/, etc.):
+2. Initialize with `uv`:
 ```bash
 uv init
 ```
+This command adds:
+- .venv/ (auto-created virtual environment)
+- pyproject.toml (project metadata & dependencies)
+- .python-version (pinned interpreter)
+- README.md, .gitignore, and a sample main.py
+
 3. Install the dependencies:
-```uv
+```bash
 uv pip install fastmcp==2.2.10
 uv add requests
 ```
 
-#### 3. Write Your MCP Server (server.py)
-1. Create the server file:
+#### 3. Build your MCP Server 
+1. Create a python file for your MCP server named `server.py`:
 ```bash
 touch server.py
 ```
-2. Edit `server.py` with the following contents:
+2. Use a file editor of your choice and copy the following content into `server.py`:
 ```bash
 import subprocess, re
 from mcp.server.fastmcp import FastMCP
@@ -89,13 +94,28 @@ if __name__ == "__main__":
 ```
 
 #### 4. Run the MCP Server
+
+Run the python script to deploy the MCP server:
+
 ```python
 uv run server.py
 ```
-By default, FastMCP will listen on port **8000** and serve your tools via **Server-Sent Events (SSE)**.
+By default, FastMCP will listen on port 8000 and serve your tools via Server-Sent Events (SSE).
+
+The output should look like:
+
+```output
+INFO:     Started server process [2666]
+INFO:     Waiting for application startup.
+INFO:     Application startup complete.
+INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
+```
 
 #### 5. Install & Configure ngrok
-1. Add ngrok’s APT repo and install:
+
+You will now use ngrok to expose your locally running MCP server to the public internet over HTTPS.
+
+1. Add ngrok’s repo to the apt package manager and install:
 ```bash
 curl -sSL https://ngrok-agent.s3.amazonaws.com/ngrok.asc \
   | sudo tee /etc/apt/trusted.gpg.d/ngrok.asc >/dev/null \
@@ -104,12 +124,16 @@ curl -sSL https://ngrok-agent.s3.amazonaws.com/ngrok.asc \
   && sudo apt update \
   && sudo apt install ngrok
 ```
+The ngrok agent authenticates with an authtoken. You will need to authenticate your account with the token which is available on the [ngrok dashboard](https://dashboard.ngrok.com/get-started/your-authtoken).
+ 
 2. Authenticate your account:
 ```bash
 ngrok config add-authtoken <YOUR_NGROK_AUTHTOKEN>
 ```
-3. Expose port 8000:
+Replace `YOUR_NGROK_AUTHTOKEN` with your token from the ngrok dashboard.
+
+3. Expose the port 8000:
 ```bash
 ngrok http 8000
 ```
-4. Copy the generated HTTPS URL (e.g. `https://abcd1234.ngrok-free.app`)—you’ll use this as your MCP endpoint.
\ No newline at end of file
+4. Copy the generated HTTPS URL (e.g. `https://abcd1234.ngrok-free.app`)—you’ll use this as your MCP endpoint.
diff --git a/content/learning-paths/servers-and-cloud-computing/mcp-ai-agent/mcp.png b/content/learning-paths/cross-platform/mcp-ai-agent/mcp.png
similarity index 100%
rename from content/learning-paths/servers-and-cloud-computing/mcp-ai-agent/mcp.png
rename to content/learning-paths/cross-platform/mcp-ai-agent/mcp.png