update abidlabs space

Co-authored-by: neonwatty

Author: burtenshaw

units/en/unit1/mcp-clients.mdx

@@ -90,7 +90,7 @@ For local servers using stdio transport, the configuration includes the command
       "transport": {
         "type": "stdio",
         "command": "python",
-        "args": ["/path/to/file_explorer_server.py"]
+        "args": ["/path/to/file_explorer_server.py"] // This is an example, we'll use a real server in the next unit
       }
     }
   ]
@@ -237,26 +237,32 @@ Tiny Agent can run MCP servers with a command line environment. To do this, we w
 
 Let's install `npx` with `npm`. If you don't have `npm` installed, check out the [npm documentation](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm).
 
+### Setup
+
+First, we will need to install `npx` if you don't have it installed. You can do this with the following command:
+
 ```bash
 # install npx
 npm install -g npx
 ```
 
-<hfoptions id="language">
-<hfoption id="python">
-
-First, install the tiny agents package:
+Then, we will need to install the huggingface_hub package with the MCP support. This will allow us to run MCP servers and clients.
 
 ```bash
 pip install "huggingface_hub[mcp]>=0.32.0"
 ```
 
-First, let's login to the Hugging Face Hub. You will need a [login token](https://huggingface.co/docs/huggingface_hub/v0.32.3/en/quick-start#authentication) to do this.
+Then, we will need to log in to the Hugging Face Hub to access the MCP servers. You can do this with the `huggingface-cli` command line tool. You will need a [login token](https://huggingface.co/docs/huggingface_hub/v0.32.3/en/quick-start#authentication) to do this.
 
 ```bash
 huggingface-cli login
 ```
 
+<hfoptions id="language">
+<hfoption id="python">
+
+### Connecting to MCP Servers
+
 Now, let's create an agent configuration file `agent.json`.
 
 ```json
@@ -291,6 +297,8 @@ First, install the tiny agents package with [npm](https://docs.npmjs.com/downloa
 npm install @huggingface/tiny-agents
 ```
 
+### Connecting to MCP Servers
+
 Make an agent project directory and create an `agent.json` file.
 
 ```bash

units/en/unit2/gradio-client.mdx

@@ -12,7 +12,38 @@ We'll connect to the MCP server we created in the previous section and use it to
 
 ## MCP Client in Gradio
 
-First, we need to install the `smolagents`, gradio and mcp-client libraries, if we haven't already:
+### Connect to an example MCP Server
+
+Let's connect to an example MCP Server that is already running on Hugging Face. We'll use [this one](https://huggingface.co/spaces/abidlabs/mcp-tools2) for this example. It's a space that contains a collection of MCP tools.
+
+```python
+from smolagents.mcp_client import MCPClient
+
+with MCPClient(
+    {"url": "https://abidlabs-mcp-tools2.hf.space/gradio_api/mcp/sse"}
+) as tools:
+    # Tools from the remote server are available
+    print("\n".join(f"{t.name}: {t.description}" for t in tools))
+
+```
+
+<details>
+<summary>Output</summary>
+<pre>
+<code>
+prime_factors: Compute the prime factorization of a positive integer.
+generate_cheetah_image: Generate a cheetah image.
+image_orientation: Returns whether image is portrait or landscape.
+sepia: Apply a sepia filter to the input image.
+</code>
+</pre>
+</details>
+
+### Connect to your MCP Server from Gradio
+
+Great, now that you've connected to an example MCP Server, let's connect to your own MCP Server from Gradio.
+
+First, we need to install the `smolagents`, Gradio and mcp-client libraries, if we haven't already:
 
 ```bash
 pip install "smolagents[mcp]" "gradio[mcp]" mcp fastmcp