In the introduction I described the architecture and the four tools the server exposes. In this post I build it.
I walk through the Python code that talks to the OAS SOAP web services and wraps them as MCP tools, install the server on oas001 as a systemd service, test it with curl and open port 8000 for the OPAF subnet only. At the end the server answers tools/list with four tools, ready for an MCP client. The next post connects OPAF to it.
This is the sixth post in the AI Data Platform series, following Just Streams: Real-Time Data Pipelines on OCI (the series intro), Setting Up the AI Data Platform Environment (stage 0), OCI Streaming and the Stream Producer (stage 1), Bronze Layer: Spark Structured Streaming (stage 2), Silver Layer: Spark Structured Streaming (stage 3), and the Comparing Bronze and Silver with DBeaver detour. Silver gave me one clean, always-current row per bus, stop, line and direction. This post covers the first half of stage 4: turning that into a live arrivals board, the kind you see on a screen at a bus stop, and putting it in front of Oracle Analytics Cloud.
This is the overview of my AI Data Platform series. It follows one real-time demo from start to finish: live London bus arrival predictions from Transport for London (TfL), streamed through OCI into Oracle AI Data Platform (AIDP), refined through a bronze, silver and gold medallion architecture, and finally analysed in Oracle Analytics Cloud, including in plain language through the Oracle Analytics MCP Server.
This post backs up a step from the rest of the AI Data Platform series. Before any bronze/silver/gold Spark job can run, or a stream producer can hand data off to something downstream, the AI Data Platform (AIDP) environment itself has to exist: a Workbench, a workspace, a catalog, compute, and a place to keep secrets. This post walks through building all of that from nothing on a brand-new OCI tenancy, using the same TfL bus-arrivals demo's naming (catalog tfl, schemas bronze/silver/gold) as the throughline.
This post explores how to connect Oracle AI Database Private Agent Factory (OPAF) with Oracle Analytics Cloud (OAC) using the Model Context Protocol (MCP). It walks through the OAuth configuration, MCP server setup, and Agent Builder flow needed to let a private OPAF agent query live, governed OAC subject areas. The result is an analytics-aware AI assistant that can discover metadata, generate Logical SQL, and answer questions using trusted enterprise data—without duplicating the semantic model or underlying data.
This post documents troubleshooting ORA-12860 deadlocks encountered while running Oracle AI Database Private Agent Factory (OPAF) ingestion workloads on Autonomous Database. It follows the investigation from recurring ingestion failures through database parallelism behavior and the eventual mitigation using NOPARALLEL settings and a logon trigger. The post also covers practical lessons from testing different ADB configurations and stabilizing the OPAF ingestion environment.
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