WMS 11919: added Oracle MCP

dev-ai-app-dev-retail/oraclemcp/oraclemcp.md

@@ -0,0 +1,300 @@
+# Build Agentic AI with Oracle MCP
+
+## Introduction
+
+You’re a developer at SeerHolding, supporting multiple SeerGroup divisions such SeerEquities, SeerRetail, SeerEnergy, SeerHealth, and many more.
+Each business unit wants smarter, AI-driven apps that interact directly with Oracle Database 23ai.
+
+Your job: build a common agentic AI foundation that lets teams query data, trigger workflows, and build their own copilots without rewriting backend logic.
+
+In this lab, you’ll connect to Oracle MCP, run it through LangChain, and interact with it via a Flask web console.
+
+You’ll see how developers can expose SQL operations and analytics as AI-discoverable tools—the building blocks for enterprise agentic systems at SeerHolding.
+
+Estimated Time: 60 minutes
+
+### Objectives
+
+- Create and store a reusable SQLcl connection for Oracle MCP.
+
+- Run LangChain as an MCP client to access Oracle Database tools.
+
+- Use a Flask web interface to interact with the database in natural language.
+
+- Observe secure logging and traceability through MCP.
+
+### Prerequisites
+
+- Completed Lab 2: Jupyter Environment Setup
+
+- (Optional) Completed Labs 3–4 on RAG and AI Vector Search
+
+## Task 1: Create a SQLcl connection for MCP
+
+Start by creating a saved connection that Oracle MCP will reuse for all tool calls.
+
+1. Open a new terminal session. Click the **blue +** in the top left corner and select **Terminal**.
+
+    ![new termincal](./images/terminal.png =50%x*)
+
+2. Run:
+
+    ```bash
+    <copy>
+    ./oraclemcp/mcpsql.sh
+    </copy>
+    ```
+
+    You should see:
+
+    ```bash
+    ✅ SQLcl connection 'jupmcp' was saved successfully.
+    ```
+
+    > What does the script do?
+
+    The script runs *sql -nolog* and executes:
+
+    ```sql
+    conn -save jupmcp -replace -savepwd ${USERNAME}/${DBPASSWORD}@"${DBCONNECTION}"
+    ```
+
+This creates a persistent, password-protected connection named jupmcp. Your MCP agent will discover and use it automatically in later steps.
+
+## Task 2: Use LangChain as the Oracle MCP Runner
+
+Next, you’ll launch LangChain as the client that connects to Oracle MCP, discovers tools, and executes your first agentic query.
+
+1. Run:
+
+    ```bash
+    <copy>
+    python ~/oraclemcp/oralanmcp.py
+    </copy>
+    ```
+
+    What the script does:
+
+    - Starts an MCP client with SQLcl: {"command":"sql","args":["-mcp"],"transport":"stdio"}.
+
+    - Initializes ChatOCIGenAI with your OCI Generative AI endpoint.
+
+    - Discover available MCP tools
+
+    - Send a natural language query: “Show all LOAN_APPLICATIONS.”
+
+2. Expected outcome:
+
+    ![response1](./images/agentresponse1.png =50%x*)
+
+✅ **Checkpoint** You successfully invoked MCP tools from LangChain and received a model-generated answer.
+
+## Task 3 (Optional): Use LangChain as the Oracle MCP Runner with a Reasoning Agent
+
+In this optional task, you’ll explore how LangChain can operate with a reasoning agent — a more advanced version of the MCP client that plans, explains, and justifies each step before acting.
+
+The reasoning agent takes longer to run, but it gives you insight into why each tool was chosen and how the decision flow unfolds inside the model.
+This is especially useful for developers who want to debug, audit, or optimize multi-tool workflows in production.
+
+Why Does It Matter?
+
+- **Reasoning agents think before they act.**
+    They plan tool usage step by step instead of responding immediately — making their logic visible and traceable.
+
+- **LangChain turns reasoning into a workflow.**
+    It coordinates multiple MCP tools automatically, showing each intermediate thought and action along the way.
+
+- **Transparency builds trust.**
+    For enterprise developers, seeing how an agent reached a result is as important as the result itself — essential for debugging, auditing, and compliance.
+
+1. Open a new terminal session. Click the **blue +** in the top left corner and select **Terminal**.
+
+    ![new termincal](./images/terminal.png =50%x*)
+
+2. Run (**Note**: It can take up to 1 minute to complete):
+
+    ```python
+    <copy>
+    python ~/oraclemcp/oralanmcp_reasoning.py
+    </copy>
+    ```
+
+3. The script will:
+
+    * Launch a new MCP client session for SQLcl
+
+    * Load all available Oracle MCP tools
+
+    * Initialize a reasoning-capable agent using the cohere.command-a-03-2025 model
+
+    * Execute the query: *Show all LOAN_APPLICATIONS*.
+
+4. Expected outcome:
+
+    ![response2](./images/agentresponse2.png =50%x*)
+
+💡**Developer Insight**
+For SeerHolding’s development teams, reasoning agents add more than verbosity. They provide explainability.
+When you deploy AI copilots for any of SeerHolding's divisions, you can trace each database interaction step by step.
+That visibility helps you debug complex chains, document behavior for audits, and build trust in production AI systems.
+
+
+✅ **Checkpoint**
+You’ve successfully run a reasoning-enabled MCP agent.
+Compare its detailed reasoning flow with the faster agent from Task 2 to see how planning affects performance and interpretability.
+
+## Task 4: Interact Through a Web Console
+
+Now you’ll use a simple web console built in Flask to interact with the Oracle MCP agent through your browser.
+
+1. Open a new terminal session. Click the **blue +** in the top left corner and select **Terminal**.
+
+    ![new termincal](./images/terminal.png =50%x*)
+
+
+2. Start the Flask app:
+
+    ```bash
+    <copy>
+    python ~/oraclemcp/app.py
+    </copy>
+    ```
+
+3. Open the web application:
+
+    ![web app](./images/webapp.png)
+
+
+    ![app](./images/agentapp.png)
+
+4. Click **Run Query** to run the default "Show all LOAN_APPLICATIONS"
+
+    ![result1](./images/result1.png)
+
+
+5. Next, enter the following query:
+
+    ```text
+    <copy>
+    Show top 5 entries from LOAN_APPLICATIONS.
+    Summarize total loans by status; return a compact table.
+    Rank the 5 highest-risk applications with reasons.
+    Please create an executive summary in Markdown.
+    </copy>
+    ```
+
+    Click Run Query.
+
+    You’ll see:
+
+    ![result1](./images/result1.png)
+
+
+💡 **Developer Insight**
+When you click New Session, you reset the conversation context and prevent older responses from influencing new prompts.
+Meanwhile, the app automatically stores your latest answer in Oracle — allowing future queries to include prior context without extra code.
+
+Here’s what this app wires together:
+
+* **Oracle MCP**: runs as the tool server through sql -mcp, exposing database operations as callable tools.
+
+* **LangChain Agent**: dynamically discovers those tools and orchestrates them at runtime.
+
+* **OCI GenAI**: uses the cohere.command-a-03-2025 model for reasoning and summarization.
+
+Together, these layers turn a standard Flask app into an agentic web interface — one that remembers, reasons, and interacts with Oracle Database 23ai in natural language.
+
+
+## Task 4: Create and Modify Database Objects via MCP
+
+1. Click **New Session** and run:
+
+    ```text
+    <copy>
+    Please create a new table using the connection available through the MCP tools. The table should be named MYTEST and should include two columns:
+    ID – an auto-incrementing primary key.
+    SUPERCOLUMN – a column of type VARCHAR(255).   
+    </copy>
+    ```
+
+
+    ![new table](./images/newtable.png)
+
+
+The agent translates your request into SQL, executes it through MCP, and confirms creation.
+This shows how developers can delegate common schema tasks to secure, explainable AI assistants without writing SQL manually.
+
+## Task 5: Explore Logging and Security
+
+Oracle MCP logs every interaction automatically. You can review the logs directly in the database.
+
+1. Open SQL Developer Web. You can find the login information in the **View login info** section of this workshop. *Your username is: admin*
+
+    ![login](./images/sqldev.png)
+
+2. In Database Actions, select the **Development** tab, then **SQL**, and click **Open**:
+
+    ![open sql](./images/opensql.png)
+
+3. Execute the following query:
+
+    ```sql
+    <copy>
+    select ID, END_POINT_NAME, LOG_MESSAGE, CREATED_ON, CREATED_BY from DBTOOLS$MCP_LOG order by ID ASC;
+    </copy>
+    ```
+
+4. Review the results and see that everything is logged.
+
+    ![mcp log](./images/log.png)
+
+You’ll see a record of each MCP action with timestamps and user IDs — ideal for auditing and traceability.
+
+
+💡 **Developer Insight**
+
+Every MCP call leaves a trace.
+Oracle MCP automatically logs the tools used, parameters passed, and responses generated, creating a transparent audit trail for every AI-driven database action.
+
+For developers, this means:
+
+* Debugging becomes data-driven. You can inspect exact tool sequences when troubleshooting complex agent runs.
+
+* Security stays enforceable. Each call links to the authenticated user, timestamp, and endpoint.
+
+* Compliance is built-in. You can prove what data was accessed, when, and by which agent — a must for regulated industries.
+
+Logging isn’t an afterthought; it’s how agentic systems at SeerHolding stay explainable, auditable, and production-ready.
+
+## Conclusion
+
+You’ve built SeerHolding’s first agentic AI development framework.
+
+You learned how to:
+
+* Connect Oracle MCP through SQLcl
+
+* Use LangChain to orchestrate Oracle Database tools
+
+* Add reasoning agents for transparent decision flows
+
+* Build a Flask-based agentic UI for natural-language queries
+
+* Leverage OCI GenAI for reasoning and summarization
+
+
+**What This Means for SeerHolding Developers**
+
+* Build once, reuse everywhere: expose SQL scripts or APIs as shared MCP tools for all SeerGroup divisions.
+
+* Integrate fast: create AI copilots that reason across SeerEquities, SeerRetail, or SeerEnergy using one architecture.
+
+* Stay compliant: every MCP call is logged, versioned, and explainable.
+
+* Deliver faster: prototype enterprise-grade AI assistants in hours, not weeks.
+
+You’ve laid the foundation for SeerHolding’s agentic AI platform: turning Oracle Database 23ai into the intelligent core of SeerGroup’s future applications.
+
+## Acknowledgements
+* **Authors** -  Kevin Lazarz
+* **Last Updated By/Date** - Kevin Lazarz, October 2025

dev-ai-app-dev-retail/workshops/tenancy/manifest.json

@@ -8,22 +8,17 @@
             "filename": "../../introduction/introduction.md"
         },
         {
-            "title": "Get Started",
-            "description": "Prerequisites for LiveLabs (Oracle-owned tenancies). The title of the lab and the Contents Menu title (the title above) match for Prerequisite lab. This lab is always first.",
-            "filename": "../../cloud-login/cloud-login.md"
-        },
-        {
-            "title": "Lab 1: Demo Guide",
+            "title": "Lab 1: Run the Demo",
             "description": "Labs that follow the introduction are numbered, starting with Lab 1",
             "filename": "../../user-story/user-story.md"
         },
         {
-            "title": "Lab 2: Connect to Development Environment",
+            "title": "Lab 2: Connect to the Development Environment",
             "description": "This is a step-by-step guide showcasing how the demo instance is navigated",
             "filename": "../../connect-to-env/connect-to-env.md"
         },
         {
-            "title": "Lab 3: Start coding with Oracle Database 23ai",
+            "title": "Lab 3: Coding Basics on Oracle Database 23ai",
             "description": "Some coding examples",
             "filename": "../../codingbasics/codingbasics.md"
         },
@@ -33,59 +28,14 @@
             "filename": "../../build/build.md"
         },
         {
-            "title": "Lab 5a Challenge Yourself: Code with AI Vector Search",
-            "description": "Labs that follow the introduction are numbered, starting with Lab 1",
-            "filename": "../../ai-exercise/ai-vector-search-challenge.md"
-        },
-        {
-            "title": "Lab 5b Step-by-step: Code with AI Vector Search",
-            "description": "Labs that follow the introduction are numbered, starting with Lab 1",
-            "filename": "../../ai-exercise/ai-vector-search-challenge-answers.md"
-        },
-        {
-            "title": "Lab 6a Challenge Yourself: Code with Property Graph",
-            "description": "This is a step-by-step guide showcasing how the demo instance is navigated",
-            "filename": "../../graph-exercise/graph-exercise.md"
-        },
-        {
-            "title": "Lab 6b Step-by-step: Code with Property Graph",
-            "description": "This is a step-by-step guide showcasing how the demo instance is navigated",
-            "filename": "../../graph-answers/graph-answers.md"
-        },
-        {
-            "title": "Lab 7a Challenge Yourself: Code with JSON Duality Views",
+            "title": "Lab 5: Build Agentic AI with Oracle MCP",
             "description": "This is a step-by-step guide showcasing how the demo instance is navigated",
-            "filename": "../../json-exercise/json-exercise.md"
+            "filename": "../../oraclemcp/oraclemcp.md"
         },
         {
-            "title": "Lab 7b Step-by-step: Code with JSON Duality Views",
+            "title": "Lab 6: Interact with Oracle Database 23ai through an MCP Server",
             "description": "This is a step-by-step guide showcasing how the demo instance is navigated",
-            "filename": "../../json-answers/json-answers.md"
-        },
-        {
-            "title": "Lab 8 Challenge Yourself: Create a Creditreport Microservice",
-            "description": "This is a step-by-step guide showcasing how the demo instance is navigated",
-            "filename": "../../microservice-creport/creditreport-exercise.md.md"
-        },
-        {
-            "title": "Lab 9 Challenge Yourself: Spatial Development with Oracle Database 23ai",
-            "description": "This is a step-by-step guide showcasing how the demo instance is navigated",
-            "filename": "../../spatial/spatial.md"
-        },
-        {
-            "title": "Lab 10a Challenge Yourself: Code with Transactional Event Queues",
-            "description": "This is a step-by-step guide showcasing how the demo instance is navigated",
-            "filename": "../../messaging-exercise/messaging-exercise.md"
-        },
-        {
-            "title": "Lab 10b Step-by-step: Code with Transactional Event Queues",
-            "description": "This is a step-by-step guide showcasing how the demo instance is navigated",
-            "filename": "../../messaging-answers/messaging-answers.md"
-        },
-        {
-            "title": "Application Architecture",
-            "description": "Review the physical and logical architecture behind the application",
-            "filename": "../../app-architecture/app-architecture.md"
+            "filename": "../../mcp/mcp.md"
         },
         {
             "title": "Need Help?",