docs: Clarify the process of "automatic" schema generation in MCP tools with Gradio

docs/01_mcp_server.md

@@ -105,6 +105,12 @@ Gradio significantly simplifies exposing a Python function as an MCP tool:
     *   **Description**: Taken from the function's docstring.
     *   **Input Schema**: Inferred from the `inputs` argument of `gr.Interface` and the function's type hints and `Args` section in the docstring.
     *   **Output Schema**: Inferred from the `outputs` argument of `gr.Interface` and the function's return type hint and `Returns` section in the docstring.
+
+> **A Note on "Automatic" Schema Generation:**
+> It's important to clarify what "automatic" means here. You, the developer, manually write your Python function (like `sentiment_analysis`) with its descriptive docstrings and type hints. You also manually define the `gr.Interface` and specify its `inputs` and `outputs` components.
+>
+> Gradio's "automatic" role is to then *interpret* all this information you've provided. It takes your Python function's definition, your docstrings, your type hints, and your `gr.Interface` configuration, and from these, it **automatically constructs the formal, standardized JSON schema that the Model Context Protocol (MCP) requires.** You don't have to write this detailed MCP-specific schema yourself; Gradio generates it for you, acting as a bridge between your Python code and the MCP standard.
+
 3.  **MCP Endpoint**: Gradio hosts the MCP service at a specific path (usually `/gradio_api/mcp/sse` relative to the base URL), handling the JSON-RPC communication required by MCP.
 
 ## 6. Conclusion for Part 1