Update README.md

README.md

@@ -28,12 +28,16 @@ Watch a demonstration of this MCP server in action with an AI agent client.
 
 **[➡️ Click here to watch the demo video](https://www.YOUR_VIDEO_LINK_HERE.com)**
 
+---
+
 ## Key Features
 
-- **Comprehensive Documentation Search:** Delivers structured summaries of official documentation, complete with overviews, installation steps, and parameter lists.
-- **In-Depth Model & Dataset Analysis:** Goes beyond basic stats to provide rich profiles of models and datasets, including descriptions, download counts, and ready-to-use code snippets.
-- **Task-Oriented Model Discovery:** Helps you find the right tool for the job by searching for models based on specific tasks like `text-classification` or `image-generation`.
-- **Live & Relevant Data:** Utilizes a combination of live API calls and intelligent web scraping to ensure the information is always up-to-date.
+-   **Comprehensive Documentation Search:** Delivers structured summaries of official documentation.
+-   **In-Depth Model & Dataset Analysis:** Provides rich profiles of models and datasets with code snippets.
+-   **Task-Oriented Model Discovery:** Helps you find the right tool for the job by searching for models based on a task.
+-   **Live & Relevant Data:** Utilizes live API calls and intelligent web scraping to ensure information is always up-to-date.
+
+---
 
 ## How to Use the Web Interface
 
@@ -41,12 +45,167 @@ The user interface is organized into clear tabs for different functions:
 
 1.  **Select a tab** at the top (e.g., "Model Information", "Documentation Search").
 2.  **Enter your query** into the textbox.
-3.  **Click the button** to get a detailed, formatted response with code examples and usage instructions.
+3.  **Click the button** to get a detailed, formatted response.
+
+---
 
 ## How to Use as an MCP Server for AI Agents
 
-This application is also a fully compliant **Model Context Protocol (MCP)** server, allowing AI agents to use its functions as tools.
+This application is a fully compliant **Model Context Protocol (MCP)** server, allowing AI agents to use its functions as tools.
 
 ### Connection Endpoint
 
-An AI agent (MCP client) can connect to this server using the following Server-Sent Events (SSE) endpoint:
+The server's public endpoint is:
+`https://agents-mcp-hackathon-huggingfacedoc.hf.space/gradio_api/mcp/sse`
+
+### Testing with a Public Client
+
+You can test this server immediately using this public MCP Client Space. Just paste the server URL above into the client's URL input field.
+
+**[➡️ Test with Public MCP Client](https://huggingface.co/spaces/ABDALLALSWAITI/MCPclient)**
+
+### Building Your Own Python Client (Locally)
+
+You can run your own agent locally that connects to this server. Follow these steps:
+
+**1. Setup Your Environment**
+
+First, create a `requirements.txt` file for the client with the following content:
+
+```text
+gradio
+smol-agents
+litellm
+````
+
+Now, set up a virtual environment and install the dependencies:
+
+```bash
+# Create and activate a virtual environment
+python -m venv venv
+source venv/bin/activate  # On Windows use `venv\Scripts\activate`
+
+# Install the required libraries
+pip install -r requirements.txt
+```
+
+**2. Set Your API Key**
+
+This client uses the Gemini API via LiteLLM. You need to set your Google API key as an environment variable.
+
+  - On **macOS/Linux**:
+    ```bash
+    export GOOGLE_API_KEY="YOUR_API_KEY_HERE"
+    ```
+  - On **Windows (Command Prompt)**:
+    ```bash
+    set GOOGLE_API_KEY="YOUR_API_KEY_HERE"
+    ```
+
+**3. Create the Client Script**
+
+Save the following code as `client_app.py`:
+
+```python
+import gradio as gr
+import os
+from smolagents import CodeAgent, MCPClient
+from smolagents import LiteLLMModel
+
+# --- Configuration ---
+# Ensure you have your GOOGLE_API_KEY set as an environment variable
+# You can get one from Google AI Studio: [https://aistudio.google.com/app/apikey](https://aistudio.google.com/app/apikey)
+API_KEY = os.getenv("GOOGLE_API_KEY")
+
+# This is the public URL of the MCP server we built.
+MCP_SERVER_URL = "[https://agents-mcp-hackathon-huggingfacedoc.hf.space/gradio_api/mcp/sse](https://agents-mcp-hackathon-huggingfacedoc.hf.space/gradio_api/mcp/sse)"
+
+if not API_KEY:
+    raise ValueError("GOOGLE_API_KEY environment variable not set. Please set your API key to run this app.")
+
+# --- Main Application ---
+try:
+    print(f"🔌 Connecting to MCP Server: {MCP_SERVER_URL}")
+    mcp_client = MCPClient(
+        {"url": MCP_SERVER_URL}
+    )
+    tools = mcp_client.get_tools()
+    print(f"✅ Successfully connected. Found {len(tools)} tools.")
+
+    # We use LiteLLM to connect to the Gemini API
+    model = LiteLLMModel(
+        model_id="gemini/gemini-1.5-flash",
+        temperature=0.2,
+        api_key=API_KEY
+    )
+    
+    # The CodeAgent is effective at using tools
+    agent = CodeAgent(tools=[*tools], model=model)
+
+    # Create the Gradio ChatInterface
+    demo = gr.ChatInterface(
+        fn=lambda message, history: str(agent.run(message)),
+        title="📚 Hugging Face Research Agent",
+        description="This agent uses the Hugging Face Information Server to answer questions about models, datasets, and documentation.",
+        examples=[
+            "What is a Hugging Face pipeline?", 
+            "Find 3 popular models for text classification", 
+            "Get the info for the 'squad' dataset",
+            "What is PEFT?"
+            ],
+    )
+
+    demo.launch()
+
+finally:
+    # Ensure the connection is closed when the app stops
+    if 'mcp_client' in locals() and mcp_client.is_connected:
+        print("🔌 Disconnecting from MCP Server...")
+        mcp_client.disconnect()
+```
+
+**4. Run the Client App**
+
+Execute the script from your terminal:
+
+```bash
+python client_app.py
+```
+
+This will launch a local Gradio interface where you can chat with an agent that uses your live MCP server.
+
+-----
+
+## Available Tools
+
+The server exposes the following tools to the AI agent:
+
+  - `search_documentation(query, max_results)`
+  - `get_model_info(model_name)`
+  - `get_dataset_info(dataset_name)`
+  - `search_models(task, limit)`
+  - `get_transformers_docs(topic)`
+  - `get_trending_models(limit)`
+
+-----
+
+## Technology Stack
+
+  - **Backend:** Python
+  - **Web UI & API:** Gradio
+  - **Data Retrieval:** Requests & BeautifulSoup
+
+-----
+
+## References & Further Reading
+
+  - **[Building an MCP Server with Gradio](https://huggingface.co/learn/mcp-course/unit2/gradio-server)** - A tutorial on the concepts used to build this server.
+
+-----
+
+## License
+
+This project is licensed under the Apache 2.0 License.
+
+```
+```