Add '/mcp' endpoint and fix spacing

mcp-server-hello-world/README.md

@@ -37,7 +37,7 @@ mcp-server-hello-world/
 ├── requirements.txt              # Python dependencies (for pip)
 ├── app.yaml                      # Databricks Apps configuration
 ├── Claude.md                     # AI assistant context and documentation
-└── README.md          
+└── README.md
 ```
 
 ## Prerequisites
@@ -89,9 +89,9 @@ The server will start on `http://localhost:8000` by default (or your specified p
 ### Accessing the Server
 
 - **MCP Endpoints**: `http://localhost:8000/mcp`
-- **Available Tools**: 
-  - `health`: Check server status
-  - `get_current_user`: Get authenticated user information
+- **Available Tools**:
+    - `health`: Check server status
+    - `get_current_user`: Get authenticated user information
 
 ## Testing the MCP Server
 
@@ -107,6 +107,7 @@ uv run pytest tests/
 ```
 
 **What the tests do:**
+
 - Automatically start the MCP server
 - Test that `list_tools()` works correctly
 - Test that all registered tools can be called without errors by invoking the `call_tools()`
@@ -123,7 +124,7 @@ uv run pytest tests/
 ```python
 from databricks_mcp import DatabricksMCPClient
 mcp_client = DatabricksMCPClient(
-    server_url="http://localhost:8000"
+    server_url="http://localhost:8000/mcp"
 )
 # List available MCP tools
 print(mcp_client.list_tools())
@@ -141,13 +142,15 @@ chmod +x scripts/dev/query_remote.sh
 ```
 
 The script will guide you through:
+
 1. **Profile selection**: Choose your Databricks CLI profile
 2. **App name**: Enter your deployed app name
 3. **Automatic configuration**: Extracts app scopes and URLs automatically
 4. **OAuth flow**: Generates user OAuth token via browser
 5. **End-to-end test**: Tests `list_tools()`, and invokes each tool returned in list_tools
 
 **What it does:**
+
 - Retrieves app configuration using `databricks apps get`
 - Extracts user authorization scopes from `effective_user_api_scopes`
 - Gets workspace host from your Databricks profile
@@ -180,11 +183,11 @@ To add a new tool to your MCP server:
 def calculate_sum(a: int, b: int) -> dict:
     """
     Calculate the sum of two numbers.
-    
+
     Args:
         a: First number
         b: Second number
-    
+
     Returns:
         dict: Contains the sum result
     """
@@ -206,13 +209,16 @@ def calculate_sum(a: int, b: int) -> dict:
 The `utils.py` module provides two helper methods for interacting with Databricks resources via the Databricks SDK Workspace Client:
 
 **When deployed as a Databricks App:**
+
 - `get_workspace_client()` - Returns a client authenticated as the service principal associated with the app. See [App Authorization](https://docs.databricks.com/aws/en/dev-tools/databricks-apps/auth#app-authorization) for more details.
 - `get_user_authenticated_workspace_client()` - Returns a client authenticated as the end user with scopes specified by the app creator. See [User Authorization](https://docs.databricks.com/aws/en/dev-tools/databricks-apps/auth#user-authorization) for more details.
 
 **When running locally:**
+
 - Both methods return a client authenticated as the current developer, since no service principal identity exists in the local environment.
 
 **Example usage in tools:**
+
 ```python
 from server import utils
 
@@ -237,20 +243,23 @@ python scripts/dev/generate_oauth_token.py \
 ```
 
 **Parameters:**
+
 - `--host`: Databricks workspace URL (required)
 - `--scopes`: Space-separated OAuth scopes (default: `all-apis offline_access`)
 - `--redirect-uri`: Callback URI (default: `http://localhost:8020`)
 
 **Note:** The script uses the `databricks-cli` OAuth client ID by default.
 
 **The script will:**
+
 1. Generate a PKCE code verifier and challenge
 2. Open your browser for authorization
 3. Capture the authorization code via local HTTP server
 4. Exchange the code for an access token
 5. Display the token response as JSON (token is valid for 1 hour)
 
 **Example with custom scopes:**
+
 ```bash
 python scripts/dev/generate_oauth_token.py \
     --host https://your-workspace.cloud.databricks.com \
@@ -272,6 +281,7 @@ uv run custom-mcp-server --help
 ```
 
 The default configuration:
+
 - **Host**: `0.0.0.0` (listens on all network interfaces)
 - **Port**: `8000` (configurable via `--port` argument)
 
@@ -338,11 +348,13 @@ Change the port in `server/main.py` or set the `PORT` environment variable.
 ### Import Errors
 
 Ensure all dependencies are installed:
+
 ```bash
 uv sync  # or pip install -r requirements.txt
 ```
 
 ## Resources
+
 - [Databricks MCP Documentation](https://docs.databricks.com/aws/en/generative-ai/mcp/custom-mcp)
 - [Databricks Apps](https://www.databricks.com/product/databricks-apps)
 - [FastMCP Documentation](https://github.com/jlowin/fastmcp)
@@ -353,5 +365,3 @@ uv sync  # or pip install -r requirements.txt
 ## AI Assistant Context
 
 See [`Claude.md`](./Claude.md) for detailed project context specifically designed for AI assistants working with this codebase.
-
-