Update README.md

Author: lara-openai

examples/mcp/building-a-supply-chain-copilot-with-agent-sdk-and-databricks-mcp/README.md

@@ -11,36 +11,22 @@ A full-stack, Databricks-themed conversational assistant for supply chain querie
 - Example agent logic and tool usage
 - Modern UX, easy local development
 
----
-
-## Project Structure
-```
-/ (root)
-├── main.py                # Example CLI agent runner
-├── api_server.py          # FastAPI backend for chat UI
-├── requirements.txt       # Python dependencies
-├── ui/                    # React frontend (Vite)
-│   ├── src/components/ChatUI.jsx, ChatUI.css, ...
-│   └── ...
-└── README.md              # (this file)
-```
-
----
 
 ## Quickstart
 
 ### 0. Databricks assets
 
 You can kick start your project with Databricks’ Supply-Chain Optimization Solution Accelerator (or any other accelerator if working in a different industry). Clone this accelerator’s GitHub repo into your Databricks workspace and run the bundled notebooks by running notebook 1:
 
-https://github.com/lararachidi/agent-supply-chain/blob/main/README.md
+https://github.com/lara-openai/databricks-supply-chain
 
 These notebooks stand up every asset the Agent will later reach via MCP, from raw enterprise tables and unstructured e-mails to classical ML models and graph workloads.
 
 ### 1. Prerequisites
 - Python 3.10+
 - Node.js 18+
 - Databricks credentials in `~/.databrickscfg`
+- OpenAI API key 
 - (Optional) Virtualenv/pyenv for Python isolation
 
 ### 2. Install Python Dependencies
@@ -87,11 +73,5 @@ npm run dev
 - To update backend agent logic, modify `api_server.py`.
 - UI styling is in `ui/src/components/ChatUI.css` (Databricks red palette).
 
----
 
-## Credits & References
-- Inspired by [Databricks Supply Chain Solution Accelerator](https://www.databricks.com/solutions/accelerators/supply-chain-distribution-optimization)
-- Uses [openai-agents-python](https://github.com/openai/openai-agents-python)
-- Databricks MCP integration via [databricks.sdk](https://github.com/databricks/databricks-sdk-py)
-- Supply-chain scope enforced by a simple LLM guardrail (see `supply_chain_guardrails.py`)
 

examples/mcp/databricks_mcp_cookbook.ipynb

@@ -87,19 +87,14 @@
     "To create this Databricks configuration profile file, run the [Databricks CLI](https://docs.databricks.com/aws/en/dev-tools/cli/) databricks configure command, or follow these steps:\n",
     "- If `~/.databrickscfg` is missing, create it: touch `~/.databrickscfg`\n",
     "- Open the file: `nano ~/.databrickscfg`\n",
-    "- Insert a profile section that lists the workspace URL and personal-access token (PAT) (additional profiles can be added at any time):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Example profile section to add in your Databricks config profile file \n",
+    "- Insert a profile section that lists the workspace URL and personal-access token (PAT) (additional profiles can be added at any time):\n",
+    "\n",
+    "\n",
+    "```bash\n",
     "[DEFAULT]\n",
     "host  = https://dbc-a1b2345c-d6e7.cloud.databricks.com # add your workspace URL here\n",
-    "token = dapi123...    # add your PAT here"
+    "token = dapi123...    # add your PAT here\n",
+    "```"
    ]
   },
   {
@@ -140,10 +135,6 @@
     "\n",
     "Alternatively, you can accelerate your setup by using a tailored version of the Databricks’ Supply Chain Optimization Solution Accelerator. To do so, you can clone this GitHub [repository](https://github.com/lara-openai/databricks-supply-chain) into your Databricks workspace and follow the instructions in the README [file](https://github.com/lara-openai/databricks-supply-chain/blob/main/README.md). Running the solution will stand up every asset the Agent will later reach via MCP, from raw enterprise tables and unstructured e-mails to classical ML models and graph workloads. \n",
     "\n",
-    "```\n",
-    "git clone https://github.com/lara-openai/databricks-supply-chain.git\n",
-    "```\n",
-    "\n",
     "If you prefer to use your own datasets and models, make sure to wrap relevant components as Unity Catalog functions and define a Vector Search index as shown in the accelerator. You can also expose Genie Spaces.\n",
     "\n",
     "The sample data mirrors a realistic pharma network: three plants manufacture 30 products, ship them to five distribution centers, and each distribution center serves 30-60 wholesalers. The repo ships time-series demand for every product-wholesaler pair, a distribution center-to-wholesaler mapping, a plant-to-distribution center cost matrix, plant output caps, and an e-mail archive flagging shipment delays. \n",