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03-GettingStarted/06-http-streaming/solution/python/README.md

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+# Running this sample
+
+Here's how to run the classic HTTP streaming server and client, as well as the MCP streaming server and client using Python.
+
+### Overview
+
+- You will set up an MCP server that streams progress notifications to the client as it processes items.
+- The client will display each notification in real time.
+- This guide covers prerequisites, setup, running, and troubleshooting.
+
+### Prerequisites
+
+- Python 3.9 or newer
+- The `mcp` Python package (install with `pip install mcp`)
+
+### Installation & Setup
+
+1. Clone the repository or download the solution files.
+
+   ```pwsh
+   git clone https://github.com/microsoft/mcp-for-beginners
+   ```
+
+1. **Create and activate a virtual environment (recommended):**
+
+   ```pwsh
+   python -m venv venv
+   .\venv\Scripts\Activate.ps1  # On Windows
+   # or
+   source venv/bin/activate      # On Linux/macOS
+   ```
+
+1. **Install required dependencies:**
+
+   ```pwsh
+   pip install "mcp[cli]" fastapi requests
+   ```
+
+### Files
+
+- **Server:** [server.py](./server.py)
+- **Client:** [client.py](./client.py)
+
+### Running the Classic HTTP Streaming Server
+
+1. Navigate to the solution directory:
+
+   ```pwsh
+   cd 03-GettingStarted/06-http-streaming/solution
+   ```
+
+2. Start the classic HTTP streaming server:
+
+   ```pwsh
+   python server.py
+   ```
+
+3. The server will start and display:
+
+   ```
+   Starting FastAPI server for classic HTTP streaming...
+   INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
+   ```
+
+### Running the Classic HTTP Streaming Client
+
+1. Open a new terminal (activate the same virtual environment and directory):
+
+   ```pwsh
+   cd 03-GettingStarted/06-http-streaming/solution
+   python client.py
+   ```
+
+2. You should see streamed messages printed sequentially:
+
+   ```text
+   Running classic HTTP streaming client...
+   Connecting to http://localhost:8000/stream with message: hello
+   --- Streaming Progress ---
+   Processing file 1/3...
+   Processing file 2/3...
+   Processing file 3/3...
+   Here's the file content: hello
+   --- Stream Ended ---
+   ```
+
+### Running the MCP Streaming Server
+
+1. Navigate to the solution directory:
+   ```pwsh
+   cd 03-GettingStarted/06-http-streaming/solution
+   ```
+2. Start the MCP server with the streamable-http transport:
+   ```pwsh
+   python server.py mcp
+   ```
+3. The server will start and display:
+   ```
+   Starting MCP server with streamable-http transport...
+   INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
+   ```
+
+### Running the MCP Streaming Client
+
+1. Open a new terminal (activate the same virtual environment and directory):
+   ```pwsh
+   cd 03-GettingStarted/06-http-streaming/solution
+   python client.py mcp
+   ```
+2. You should see notifications printed in real time as the server processes each item:
+   ```
+   Running MCP client...
+   Starting client...
+   Session ID before init: None
+   Session ID after init: a30ab7fca9c84f5fa8f5c54fe56c9612
+   Session initialized, ready to call tools.
+   Received message: root=LoggingMessageNotification(...)
+   NOTIFICATION: root=LoggingMessageNotification(...)
+   ...
+   Tool result: meta=None content=[TextContent(type='text', text='Processed files: file_1.txt, file_2.txt, file_3.txt | Message: hello from client')]
+   ```
+
+### Key Implementation Steps
+
+1. **Create the MCP server using FastMCP.**
+2. **Define a tool that processes a list and sends notifications using `ctx.info()` or `ctx.log()`.**
+3. **Run the server with `transport="streamable-http"`.**
+4. **Implement a client with a message handler to display notifications as they arrive.**
+
+### Code Walkthrough
+- The server uses async functions and the MCP context to send progress updates.
+- The client implements an async message handler to print notifications and the final result.
+
+### Tips & Troubleshooting
+
+- Use `async/await` for non-blocking operations.
+- Always handle exceptions in both server and client for robustness.
+- Test with multiple clients to observe real-time updates.
+- If you encounter errors, check your Python version and ensure all dependencies are installed.
+